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i^ASA Contractor Report 181705 



CiJ/08 0105575 
J. S. Gibson 
A. Ad ami an 

N d 9 - 1 1 7 5 

Contract Nos. NAS 1-1 7070 and NAS 1-18 107 
August 1988 

NASA Langley Research Center, Hampton, Virginia 23665 

Operated by the Universities Space Research Association 


National Aeronautics and 
Space Administration 

unQwy RaMMch Oantaf 
Hannpton, Virginia 23665 


J. S. Gibson and A. Adamian 

Mechanical. Aerospace and ^'^-^^^^/"^^"""'"^^A 
University of California, Lo.; Angeles, CA 9002^ 


XMs paper presents approximation the - ^^ r;;7irtrrbut:d 

Gaussian optimal control P-^^- /^^^ 'operators "he main purpose of the 
n,odels have bounded input ^nd output oP^^atjrs ^^^^^^^ ^^^, ^^^,,^,. 
theory is to guide the design of finite dt.ensio P ^^^^^^ ^^^^^^_ 

n>ate closely the optimal compensator ^^^^^^^^\^\^ ^,,,J^^ dimensional 
quadratic control problem lies in ^^^ ^'^^"^^^^ J^ ^^ ^he paper approximates 
Riccatl operator equation The ^'°^:^l^^ If flniL dimensional LQG 
the infinite dimensional LQG P^^^^^^^^^^V dimensional , usually finite element 
problems defined for a ^^^^^^ J^^'^^.'Alfted'^L^^ 'of the structure. Two 
MccTt"Vatr^^^eta^tn\"neferm\" t^e solution to each approximating problem. 

Tbe finite dimensional equations for numeic la ro^ 
veloped, including formulas °^. ^^/f,; t^'^f.^'pLJLn of gains based on dif- 
to their functional ^^P^^^^f ^^^°^. ^° ^^^ ^e of the approximating control and 
ferent orders of approximation. C^";;//;;^,;,,;' dimensional compensators is 
estimator gains and of the co'rrespondi g fimte ^ ^^^^^^ ^..^uced 

studied. Also, convergence and stability of the c ^^ convergence 

with the finite dimensional ^^^^^^^.^l?"' J^ZJ^^f the finite dimensional 
theory is based on the convergence of the solutions ^^^^^ ^^^^^^^ ^^^^_ 

f.-:r rn-t^\\rexrpi:ti;t°rflLite^htim, . rotatmg rigid body, and a 
lumped mass is given. 

, , v^ the National Aeronautics and Space Administra- 

This research was supported \^^^_^%f'^^^ NASl-18107 while the first author 
tion under NASA Contract Nos. NASI 17070 and N^b ^^ g^i^^ee and 

was in residence at the Institute for ^°''^^'-^' ^^^'^ ^ vA 23665. Also. 
Engineering (ICASE), NASA ^angleyResearhCener Hampton. ^^ ^^^ ^^^^^ ^^^^^ 

rPOSrG^ra^nrBro?0.^rd°"yThV.Trpuls^:n Laboratory. Pasadena, CA under 

Grant 957114. 



1 . Introduction - 

2. The Control System ^^ 

2.1 The Energy Spaces and the First-order Form 

of the System 

The Elastic-strain-energy Space V and the 

Total-energy Space E j^ 

The Damping Functional and Operator 15 

The Semigroup Generator ^ 

2.2 The Adjoint of ^ JJ 

2.3 Exponential Stability 

3. The Optimal Control Problem ^^ 

3.1 The Generic Optimal Regulator Problem 22 

3.2 Application to Optimal Control of 

Flexible Structures 


4 . The Approximation Scheme 

4.1 Approximation of the Open- loop System 32 

4.2 The Approximating Optimal Control Problems 3^ 

5. Convergence 

5 .1 Generic Approximation Results 

5.2 Convergence of the Approximating Optimal 

Control Problems of Section 4.2 

Exam pi e 

6.1 The Control System 

6.2 The Optimal Control Problem 

6 .3 Approximation 

6 .4 Numerical Resul ts 




7. The Optimal Infinite Dimensional Estimator, 

Compensator and Closed-loop System 71 

7 .1 The Generic Problem .'.'.*.'.'.*.'.'.'.'!,'.'.'.' .' 71 

7.2 Application to Structures '.'.'.'.'. 76 

8. Approximation of the Infinite Dimensional Estimator .. 79 

8.1 The Approximating Finite Dimensional Estimators . 79 

8.2 Stochastic Interpretation of the Approximating 
Estimators oo 

8.3 The Approximating Functional Estimator ' Gai ns ".'.'! .* 85 

8 .4 Convergence " g- 

9. The Finite Dimensional Compensators and Realizable 
Closed-loop Systems 9q 

9.1 Closing the Loop .*!."!!!.'!.'.'!.'."*" 90 

9.2 Convergence of the Closed-loop Systems".'!.'.*!.'.'!.*.' 92 

9.3 Convergence of the Compensator Transfer 

Functions pg 

10. Closing the Loop in the Example 99 

10.1 The Estimator Problan !!!!!!!!!!! 99 

10.2 Approximation of the Optimal Compensator"!!!!!!!! 107 

10.3 Comments on the Structure and Dimension of the 
Implementable Compensators I09 

11. Conclusions ,,, 

References , - - 

Appendix: Errata for [Gl] Hg 



„..e„t ye... .ave seen inereaslng researoh In active oontrcl of nexi.le 
.tnuotures. The pri.a,, .oUvatlon Tor U,!., research 1. eontroX of large 
ne=..Xe ae.o.pace structure. wMc. are .co.ins larger ana .ore ne,i.le at 
^e sa^e ti.e that their .rfor.a„oe r.uir«.ent3 are .coding .ore stringent, 
.or e^Ple. m tracking ana other applications, satellites wit. large enten- 
te, solar collectors ana other flexible components .ust perfor. fast slew 
^.neuvers while maintaining ti*t control over the vihrations of their fle,i- 
.ie el^ents. Both of these oonflicti,* ot.ectives can he achievea only with 
. sophisticatea ocntroUer. .ere are appaications also to control of rototic 
manipulators with flexible lin.s, ana Poss.hl. to stahilization of large civil 
engineering structures such as long hriage, a„a tall huiiaings. 

^e first question that must he answerea when aesigning a controller for 

, .,.ther a finite almensional moael is sufficient as a 
a flexible structure is whether a unite ux 

^sis for a controller that will proauoe the rcuirea perfor-anoe. or is a 
aistributea moael necessary, While some structures can be moaelea well by a 
f ixea n«ber of aomi.nt moaes, there are structures whose flexible character 
oan . capturea sufficiently for precise control only by a aistributea moael. 
Still others - perhaps most of the aeros.ce structures of the future - can 
. moaelea sufficiently for control pur^ses by some finite almensional 
approximation, but an aae,uate approximation may . impossible to aete^ine 
.fore aesign of the »„troller. or .mP^nsator. This pap.r aeals with struc- that are flexible enou* to r.uirc a aistributea moael in the aesign of 
an optimal LQG compensator. 

The Unea^,„ optical «.„t.ol probl- for distributed, o. 
inflate dX^enalcnal,»a Is a generalization to „llt«.t space of the now 
olassioax WO p.oM» for finite dimensional s,st«s. ,.e solution to the 
infinite dimensional prohl. nelds an Infinite dimensional stat^astlmaton- 
based oom^nsator, which Is optimal m the context of this pape.. By a 
=epa.atlon principle tBl, «,, the pnohl» .educes to a deterministic Unea,- 
quadratic optimal contnol p.o„- and an optimal estimation, o. filtering 
pnohlem with gausslan white noise. l„ i„f.,,e dimensions, the control syst. 
dynamics are represented h, a semigroup of hounded linear operators Instead of 
the matrix exponential operators In flMte dimensions, and the plant noise 
process may he an Infinite dimensional random process, ^e solutions to ^th 
the control and fnterl^ prohlema Involve Hlccatl operator equations, which 
a.e generalizations of the «lccatl matrix equations In the finite dimensional 
oase. c».re„t results on the Infinite dimensional UiO prohl« are most com- 
plete for problems where the Input and measur.ent curators are bounded, as 

this paper requires throuchouf T^,^o k^ ^ ^ 

cnroughout. This boundedness also permits the strongest 

approximation results here Pn». «^i»«. ^ 

here. For related control problans with unbounded input 

and measurement, see [C3. C5 , LI, L2] , 

our prlman- objective In this paper Is to approximate the optimal infl^ 
ite dimensional UO com^nsator for a districted model of a flexible struc- 
ture with finite dlmenslo,.! com^nsators based on approxlmaUons to the 
atruoture. and to have these finite dimensional «,mpensators produce near 
optimal performance of the closed-loop syst.. We discuss how the gains that 
determine the finite dimensional «.mP.nsators converge to the gains that 
determine the Infinite dimensional .mp^nsator. and we examine the sense In 

which the finite dimensional compensators converge to the infinite dimensional 
compensator. With this analysis, we can predict the performance of the 
closed-loop system consisting of the distributed plant and a finite dimen- 
sional compensator that approximates the infinite dimensional compensator. 

Our design philosophy is to let the convergence of the finite dimensional 
compensators indicate the order of the compensator that is required to produce 
the desired performance of the structure. The two main factors that govern 
rate of convergence are the desired performance (e.g., fast response) and the 
structural damping. We should note that any one of our compensators whose 
order is not sufficient to approximate the Infinite dimensional compensator 
closely will not in general be the optimal compensator of that fixed order; 
i.e., the optimal fixed-order compensator that would be constructed with the 
design philosophy in [B7, B8] . But as we increase the order of approximation 
to obtain convergence, our finite dimensional compensators become essentially 
identical to the compensator that is optimal over compensators of all orders. 

An important question, of course, is how large a finite dimensional com- 
pensator we must use to approximate the infinite dimensional compensator. In 
[G6, G7, G8, Ml], we have found that our complete design strategy yields com- 
pensators of reasonable size for distributed models of complex space struc- 
tures. This strategy in general requires two steps to obtain an implementable 
compensator that Is essentially Identical to the optimal infinite dimensional 
compensator: the first step determines the optimal compensator by letting the 
finite dimensional compensators converge to it; the second step reduces, if 
possible, the order of a large (converged) approximation to the optimal com- 
pensator. The first step, which is the one involving control theory and 

approximation theory for distributed systems, is the subject of this paper. 
For the second step, a simple modal truncation of the large compensator some- 
times is sufficient, but there are more sophisticated methods in finite dimen- 
sional control theory for order reduction. For example [G8, Ml], we have 
found balanced realizations [M2] to work well for reducing large compensators. 

The approximation theory in this paper follows from the application of 
approximation results in [B6, G3 , G4] to a sequence of finite dimensional 
optimal LQG problems based on a Ritz-Galerkin approximation of the flexible 
structure. For the optimal linear— quadratic control problem, the approxima- 
tion theory here is a substantial improvement over that in [Gl] because here 
we allow rigid-body modes, more general structural damping (including damping 
in the boundary), and much more general finite element approximations. These 
generalizations are necessary to accommodate common features of complex space 
structures and the most useful finite element schemes. For example, we write 
the equations for constructing the approximating control and estimator gains 
and finite dimensional compensators in terms of matrices that are built 
directly from typical mass, stiffness and damping matrices for flexible struc- 
tures, along with actuator influence matrices and measurement matrices. 

For the estimator problem, this paper presents the first rigorous approx- 
imation theory. (We have used less complete versions of the results in previ- 
ous research [G6 , G7 , G8, Ml].) As in the finite dimensional case, the infin- 
ite dimensional optimal estimation problem is the dual of the infinite dimen- 
sional optimal control problem, and the solutions to both problems have the 
same structure. Because we exploit this duality to obtain the approximation 
theory for the estimation problem from the approximation theory for the 

opti.^ control probla., the in thl= papor is al.o=t entirely deter- 
Mni^tio. we di.ou=s the .toohastlc interpretation of the estimation prohle. 
and the approximating state estimators briefly, but -e are concerned mainly 
„ith deterministic ..uestions about the structure and convergence of approxima- 
tions to an infinite dimensional oom^nsator and the performance - especially 
stability - Of the closed-loop syst»s produced by the approximating comper. 


Next, an outline of the paper should aelp. Tn. paper has two main parts. 
.hiC correspond roughly to the separation of the optimal UJO probl«. into an 
optimal linea^^uadratic regulator prohle. and an optimal state estimation 
problem. The first half. Sections 2 through 6. deal with the control system 
and the optimal resistor problem. Sections 7 throu* i« treat the state 
estimator and the compensator that is formed by applying the control law of 
the first half of the paper to the output of the estimator. 

section 2 defines the abstract model of a flexible structure and the 
energy spaces to he used throughout the paper. We ass«e a finite n«ber of 
actuators, since this is the case in all applicaUons. and we ass-e that the 
actuator influence operator is funded. Section 2 also establishes certain 
msa-ematical properties of the o,...loop system that are useful in control and 
approximation. To our ^owledge. the exponential stability theory in Section 
2.3 is a new result, and we find it interesting that such a simple Lyapuno, 
functional accommodates such a general damping model. 

section 3 discusses the linear-quadratic optimal control probla. for the 
aistrit^ted model of the structure and establishes some estimates inyolving 

lx,u„ds on solutions to Infinite dimensional Rlooatl equations and oper^loop 
and olosed-loop decay rates. We need these estimates for the subsequent 
approximation theory. To get the approximation theory for the estimation 
problem, we have to give certain results on the control proble. m a more ge,. 
eral form than would be necessary were we Interested only In the control proh- 
1- for flexible structures, aerefore. In Section 3, as In Sections 5 and 7. 
we first give some generic results applicable to the UO problem for a variety 
Of distributed systems and then apply the generic results to the control of 
flexible structures. 

Because we assume a finite number of actuators and a bounded Input opera- 
tor, the optimal feedback control law consists of a finite number of bounded 
linear functlonals on the state space, which Is a Hubert space. ,„ls means 
that that the feedback law can be represented In terms of a finite number of 
vectors, which wa call functional control gains, whose Inner products with the 
generalized displacement and velocity vectors define the control law. For any 
flnlt^rank. bounded linear feedback law for a control system on a Hubert 
space, the existence of such gains Is obvious and well known. A functional 
control gain for a flexible structure will have one or more distributed com- 
ponents, or kernels, corresponding to each distributed component of the struc- 
ture and scalar components corresponding to each rigid component of the struc- 

we introduce the functional control gains at the end of Section 3, and we 
introduce analogous functional estimator gains In Section 7. ihe functional 
gains Play a prominent roll l„ our a^lysls. They give a concrete representa- 
tion Of the infinite dimensional compensator and provide a criterion for con- 

vergence of the approximating finite dimensional compensators. 

We develop the approximation scheme for the control problem in Section 4. 
The idea is to solve a finite dimensional linear-quadratic regulator problem 
for each of a sequence of Ritz-Galerkin approximations to the structure. We 
develop the approximation of the structure in Section 4 .1 and prove conver- 
gence of the approximating open-loop systems. The approximation scheme 
includes most finite element approximations of flexible structures. For con- 
vergence, we use the Trotter-Kato semigroup approximation theorem, which was 
used in optimal open-loop control of hereditjiry systems in [B5] and has been 
used in optimal feedback control of hereditary, hyperbolic and parabolic sys- 
tems in [B6, Gl, G3] and other papers. The usual way to invoke Trotter-Kato 
is to prove that the resolvents of the approximating semigroup generators con- 
verge strongly. To prove this, we introduce an inner product that involves 
both the strain-energy inner product and the damping functional, and show that 
the resolvent of each finite dimensional semigroup generator is the projec- 
tion, with respect to this special inner product, of the resolvent of the ori- 
ginal semigroup generator onto the approximation subspace. The idea works as 
well for the adjoints of the resolvents, and when the open-loop semigroup gen- 
erator has compact resolvent, it follows from our projection that the approxi- 
mating resolvent operators converge in norm. 

In Section 4.2, we define the sequence of finite dimensional optimal con- 
trol problems, whose solutions approximate th«i solution to the infinite dimen- 
sional problem of Section 3. The solution to each finite dimensional problem 
is based on the solution to a Riccati matrix equation, and we give formulas 
for using the solution to the Riccati matrix equation to compute approxima- 

tlons to the functional control gains as linear combinations of the basis vec- 

Section 5.1 summarizes some generic convergence results from [B6, G3 , G4] 
on approximation of solutions to infinite dimensional Riccati equations. Sec- 
tion 5.2 applies these generic results to obtain sufficient conditions for 
convergence ~ and nonconvergence ~ of the solutions of the approximating 
optimal control problems in Section 4.2. A main sufficient condition for con- 
vergence is that the structure have damping, however small, that makes all 
elastic vibrations of the open-loop system exponentially stable. This is a 
necessary condition if the state weighting operator in the control problem is 

In Section 6. we present an example in which the structure consists of an 
Euler- Bernoulli beam attached on one end to a rotating rigid hub and on the 
other end to a lumped mass. We emphasize the fact that we do not solve, or 
even write down, the coupled partial and ordinary differential equations of 
motion. For both the definition and numerical solution of the problem, only 
the kinetic and strain energy functional s and a dissipation functional for the 
damping are required. We show the approximating functional control gains 
obtained by using a standard finite element approximation of the beam, and we 
discuss the effect on convergence of structural damping and of the ratio of 
state weighting to control weighting in the performance index. As suggested 
by a theorem in Section 5. the functional gains do not converge when no struc- 
tural damping is modeled. 

In Section 7, we begin the theory for closing the loop on the control 
system. We assume a finite number of bounded linear measurements and 


construct the optimal state estimator, which is infinite dimensional in gen- 
eral. The gains for this estimator are obtained frcan the solution to an 
infinite dimensional Riccatl equation that has the same form as the infinite 
dimensional Riccati equation in the control problem. We call these gains 
functional estimator gains because they are vectors in the state space. 

Since the approximation issues that this paper treats are fundamentally 
deterministic, we make the paper self contained by defining the infinite 
dimensional estimator as an observer, although the only justification for cal- 
ling this estimator and the corresponding compensator optimal is their 
interpretation in the context of stochastic estimation and control. We dis- 
cuss the stochastic interpretation but do not use it. We say estimator and 
observer interchangeably to emphasize the deterministic definition of the 
estimator here. 

With the optimal control law of Section 3 and the optimal estimator of 
Section 7, we construct the optimal compensator, which also is infinite dimen- 
sional in general. The transfer function of this compensator is irrational, 
but it is still an m(number of actuators) p (number of sensors) matrix func- 
tion of a complex variable, as in finite dimensional control theory. The 
optimal closed-loop system consists of the distributed model of the structure 
controlled by the optimal compensator. 

Approximation of the optimal compensator is based on approximating the 
infinite dimensional estimator with the sequence of finite dimensional estima- 
tors defined in Section 8.1. The gains for the approximating estimators are 
given in terms of the solutions to finite dimensional Riccati equations that 
approximate the infinite dimensional Riccc^ti equation in Section 7. Although 

defined as observers, these finite dimensional estimators can be interpreted 
as Kalman filters, as shown in Section 8.2. In Section 8.3. we give formulas 
for finite dimensional functional estimator gains that approximate the func- 
tional estimator gains of Section 7. These approximating estimator gains 
indicate how closely the finite dimensional estimators approximate the infin- 
ite dimensional estimator. In Section 8.4, we apply the Riccati equation 
approximation theory of Section 5 to describe the convergence of the finite 
dimensional estimators. 

Most of the results in Section 8 are analogous to results for the control 
problem and follow from the same basic approximation theory, but certain 
differences require careful analysis. There is an important difference in the 
way that the Riccati matrices to be computed are defined in terms of the fin- 
ite dimensional Riccati operators. Indeed, the Riccati matrix equations to be 
solved numerically might seem incorrect at first. To demonstrate that the 
finite dimensional estimators that we define in Section 8.1 are natural 
approximations to the optimal infinite dimensional estimator, we show in Sec- 
tion 8.2 that each finite dimensional estimator is a Kalman filter for the 
corresponding finite element approximation of the flexible structure. The 
brief discussion in Section 8.2 is the only place in the paper where stochas- 
tic estimation theory is necessary, and none of the analysis in the rest of 
the paper depends on this discussion. 

In Section 9.1, we apply the nth control law of Section 4 to the output 
of the nth estimator to form the nth compensator. (The order of approximation 
is n.) The nth closed-loop system consists of the distributed model of the 
structure controlled by the nth compensator. Since each finite dimensional 


estimator is realizable, the nth compensator and the nth closed-loop system 
are realizable. In Section 9.2, we discuss how the sequence of realizable 
closed-loop systems approximates the optimal closed-loop system. Probably the 
most important question here is whether exponential stability of the optimal 
closed-loop system implies exponential stability of the nth closed-loop system 
for n sufficiently large. We have been ab] e to prove this only when the 
approximation basis vectors are the natura;. modes of undamped free vibration 
and these modes are not coupled by structural damping. That this stability 
result can be generalized is suggested by the results in Section 9.3, which 
describe how the transfer functions of the finite dimensional compensators 
approximate the transfer function of the optimal compensator. 

In Section 10, we complete the compensator design for the example in Sec- 
tion 6. Assuming that white noise corrupts the single measurement and that 
distributed white noise disturbs the structure, we compute the gains for the 
finite dimensional estimators and show the functional estimator gains. As in 
the control problem, the functional gains do not converge when no damping is 
modeled. We apply the control laws computed in Section 6 to the output of the 
estimators in Section 10 to construct the finite dimensional compensators, and 
we show the frequency response of these a:.mpensators. As predicted by Section 
9.3, the frequency response of the nth conpensator converges to the frequency 
response of the optimal infinite dimensional compensator as n increases. In 
Section 10.3, we discuss the structure and dimension of the finite dimensional 
compensator that should be implanented. 

We conclude in Section 11 by discussing where the approximation theory 
presented in this paper is most complete and what further results would be 
most important. 


2. The Control System 

We consider the gystem 

Mt) + D^M + Aox(t) = Bou(t). t > 0. 

where x(t) is in a real Hilbert space H and u(t) is in r" for some finite m. 
The linear stiffness operator A, is densely defined and selfadjoint with com- 
pact resolvent and at most a finite number of negative eigenvalues. We will 
postpone discussion of the damping operator D^ momentarily, except to say that 
it is symmetric and nonnegative. l^e input operator B^ is a linear operator 
from r" to H, hence bounded. 

By jiatuiai Sfides. v£e jiill aaaa iiie eig^qyeotors <fi^ sif IM eigenyalue 

From our hypotheses on A^. we know that these eigenvalues form an infinitely 
increasing sequence of real numbers, of which all but a finite number are 
positive. Also, the corresponding eigenvectors are complete in H and satisfy 

(These properties of the eigenvalue problem (2.2) are standard. See. for 

example. [Bl] . [Kl] .) For A,. > O m - 'TT ^c, , v>,4. 

' A.J / u, u>j - f Xj is a naiUJCai freaueno.v . 


Remark 2.2. Our analysis includes the ^stem 

• * • 

^t.x(t) + D-x(t) + A.x(t) = B-u(t), t > 0, 

" " " (2.l') 

where the mass operator Mq is a selfadjoint, bounded and coercive linear 

operator on a real Hilbert space Hq. The operators Aq, Bq and D^ in (2.1') 

have the same properties with respect to H^ that the corresponding operators 

in (2.1) have with respect to H. To include (2.1') in our analysis, we need 

only take H to be H^ with the norm- equivalent inner product <*'*^h '^ 

<Mq\'>jj , and multiply (2.1') on the left by vi^^. In H, the operator M^^f^Q 

is selfadjoint with compact resolvent, and M^ Dq is symmetric and nonnegative. 
With no loss of generality then, we will refer henceforth only to (2.1) and 
assume that the H-inner product accounts for the the mass distribution. D 

2.1 The Energy Spaces and the First-order Form of the System 

The Elastic-Strain - Energy Space V and Total - Energy Space E 

We choose a bounded, selfadjoint linear operator Aj^ on H such that Aq = 
Aq + k. is coercive; i.e., there exists p / for which 

<AqX,x>jj I p||x||^. xbKAq) = D(Aq). 


Since Aq is bounded from below, there; will be infinitely many such Aj^'s. 
In applications like our example in Section 6, It is natural to select for A^ 
an operator whose null space is the orthogonal complement (in H) of the eigen- 
space of Aq corresponding to nonpositive eigenvalues. Obviously, any A^ that 
makes Aq coercive must be positive definite on the nonpositive eigenspace of 



With k^ chosen, we define the Hilbert space V to be the completion of 

D(Aq) with respect to the inner product <v-,,V2>y = <A.qV^,V2>u, v- and 

~l/2 '^'1/2 ~i/2 

v, e D(Aq). Note that V = D(Aq ) and <Vj^,V2>y = <Aq v-,Aq V2>y. (Since A^ 

is a bounded operator on H, different choices of k^ yield V's with equivalent 

norms, thus containing the same elements) . 

In the usual way, we will use the imbedding 

VC H = H' C V, 

where the injections from V into H and from H into V are continuous with 
dense ranges. We denote by Ay the Riesz map from V onto its dual V; i.e., 

<v,v. > = (AyV, )v, V. ,v e V. 


Then A_ is the restriction of Ay to D(Aq) in the sense that 

(AyVj)v = <v,AqVj^>jj, Vj e D(Aq), v e V. 


Now we define the total energy space E = V x h, noting that when A- is 
coercive and x(t) is the solution to (2.1), then | | (x(t) ,x(t)) | |„ is twice the 
total energy (kinetic plus potential) in the system. We want to write (2.1) 
as a first-order evolution equation on E. To to this, we must determine the 
appropriate semigroup generator for the open-loop gystem. We will derive this 
generator by constructing its inverse explicitly, and then we will try to con- 
vince the reader that we do have the appropriate open-loop semigroup genera- 
tor. The approach seems mathematically efficient, and we will need the 
inverse of the generator for the approximation scheme. First, we must state 
our precise hypotheses on damping and discuss its representation. 


The Damping Functional and Operator 

Actually, we do ufit require an operator D^ defined from some subset of H 
into H. Bather, we assume only that there exists a damping functional 

such that dp is bilinear, symmetric, continuous (on V X V) and nonnegative. 

If we have a symmetric, nonnegative dai;iping operator Dq defined on D(Ao) 
such that Dq is bounded relative to Ap. then <DoV3^.V2>h defines a bilinear, 
symmetric, bounded, nonnegative functional on a dense subset of V X V. In 
this case, the unique extension of this functional to V X V is d_. (That D 
being A^-bounded implies continuity of <Dq','>^ with respect to the V-norm 
follows from [Kl. Theorem 4.12, p. 292].) 

Under our hypotheses on dg, there is a unique bounded linear operator A^ 
from V into V such that 

dQ(v,v^) = (AjjV^)v, Vj,v e V. 


The operator (A^ A^) is then a bounded linear operator frcan V to V, and 
(Ay Ajj) is selfadjoint (on V) because dg is symmetric. Also 

dQ(v.v^) = <vA~\v^>^ = <A-\y.v^>^ V V e V. 


Remark 2.2. We chose to begin our description of the control system model 
with (2.1) because its form is familiar in the context of flexible structures. 
The stiffness operator A^, for example, is the infinite dimensional analogue 
of the stiffness matrix in finite dimensional structural analysis. In appli- 
cations like the example in Section 6, though it is often easier to begin 


with a strain-energy functional from which the correct strain-energy inner 
product for V is obvious. The stiffness operator is defined then in terms of 
the Riesz map for V (see [S3] for this approach), rather than V being defined 
in terms of the stiffness operator; specifically, t^ is defined by (2.6) with 
D(A ) = A'^^'H. Either way, the relationship between A^ and V is the same. But 
the only thing that needs to be computed in applications is the V-inner pro- 
duct; an explicit Aq need not be written down. O 

The Semigroup Generator 

We define A~-^ e L(E,E) by 


-Av ^D "%) 


This operator is clearly one-to-one, and its range is dense, since V is dense 
in H and D(Aq) is dense in V. Now, we take 

Direct calculation of the inner product shows 

<^~^ I ' I >^ = -dn(h.h), 

h h E (2.12) 

so that t is dissipative with dense domain. Also, since D(A ) = E, A is max- 
imal dissipative by [Gl. Theorem 2.1]. Therefore, If generates a Cq- 
contraction semigroup on E. 

Finally, the open-loop semigroup generator is 


A = A + 

X s] ■"*' ■ «'' 


where A. is the bounded linear operator discussed above. With 

B = I " I e L(R°.E). 



the first-order form of (2.1) is 

z(t) = Az(t) + Bu(t), t>0 

where z = (x,x) e E. 


To see that A is indeed the appropriate open-loop semigroup generator, 
suppose that Aq is coercive (so that Aj^ = 0) and that we have a symmetric, 
nonnegative A.-bounded damping operator Dq. Then the appropriate generator 
should be a maximal dissipative extension of the operator 

o r . - T o 


° _J 1 . D(A) = D(Aq) XD(Aq) 


It is shown in [Gl, Section 2] that A has a unique maximal dissipative exten- 

sion, and it can be shown easily that the A defined above is an extension of A 

after noting that, in the present case. 

(Av\)Id(Ao) = ^\' 


We should note that Showalter [S3, Chapter VI] elegantly derives a 
semigroup generator for a class of second-order systems that includes the 
flexible- structure model here. The presentation here is most useful for our 
approximation theory because of the explicit construction of the inverse of 


the semigroup generator. For the purposes of this paper, we do not need to 
characterize the operator A itself more explicitly, but we should make the 
following points. 

First, from A we see 

D(A) = {(x.x): X e V, X + ^~^/\^x e D(Aq)}. 


In many applications, especially those involving beams, the "natural boundary 
conditions" can be determined from (2.18) and the boundary conditions included 
in the definition of DA^) . in the case of a damping operator that is bounded 
relative to aJ ^, D(A) = D(Aq)xV. If the damping operator is bounded rela- 
tive to A^ for n < 1, then A has compact resolvent. 

In many structural applications, the open-loop semigroup is analytic, 
although this has been proved only for certain important cases. Showalter 
obtains an analytic semigroup when the damping functional is V-coercive; for 
example, when there exists a damping operator D^ that is both A^-bounded and 
as strong as A^. Such a damping operator results fran the Kelvin- Voigt 
viscoelastic material model. Also, it can be shown that the semigroup is ana- 
lytic for a damping operator equal to c^A^ for 1/2 <. m i 1 and c^ a positive 
scalar. The case \i = 1/2, which produces the same damping ratio in all modes, 
is especially common in structural models, and Chen and Russell [CI] have 
shown that the semigroup is analytic for a more general class of damping 
operators involving A^'^. 

Finally, we can guarantee that the open-loop semigroup generator is a 
spectral operator (i.e., its eigenvectors are complete in E) only for a dampr- 
ing operator that is a linear combination of an H-bounded operator and a 


fractional power of A.. However, nowhere do we use or assume anything about 
the eigenvectors of either the open-loop or the closed-loop semigroup genera- 
tor. The natural modes — of undamped free vibration — in (2.2) are always 
complete in both H and V. 

2.2 The Adjoint of A 

-1 **-* 

Since (A^ Aj,) is selfadjoint on V, direct calculation shows that A 

(a""*") — the adjoint of A~ with respect to the E- inner product — is 


-I ^ 


Then ^* = (iT )~^ . Having TJ" explicitly facilitates proving strong conver- 
gence for approximating adjoint semigroups. 

2.3 Exponential Stability 

The following theorem says that, if there are no rigid-body modes and if 
the damping is coercive (basically, all structural components have positive 
damping), then the open-loop system is uniformly exponentially stable. That 
the decay rate given depends only on the lower bound for the stiffness opera- 
tor and the upper and lower bounds for the damping functional is essential for 
convergence results for the approximating optimal control problems of subse- 
quent sections. The theorem is a generalization of Theorem 6.1 of [Gl] to 
allow more general damping, but the proof is entirely different and much 
nicer. The current {X'oof uses an explicit Lyapunov functional for the homo- 
geneous part of the gystem in (2.15). Recall that TO is the open-loop semi- 
group, with generator A, and E is the total energy space V x H. 


Theorem 2.3 . Suppose that A^ and d^ are H-coercive. Let p be the positive 
constant in (2.4), and let 6- and 8, be positive constants such that 

SqII^IIh ^ **0<^'V^ iS^llvllJ. V e V. 



MT(t)|| i (1 +^ + 8^8q/2)^^^ exp[-t/(^ +J;.+ 8^)] .tlO. 

° (2.21) 

Proof . For y > max{ ^ , ■^) . define Q e L(E) as 

Q = 

(yl+Ay^Ap) A^^ 

I yi 


Since (Ay A^) is selfadjoint and nonnegative on V, Q is selfadjoint and coer- 
cive on E. Define the functional p(*) on E by 

p(z) = <Qz.z>g = ydlxllj + llxllj) + 2<x,x>jj + do(x,x) 

where z = (x,x). Frcan (2.4), we have 


2|<x.x>jj| < ^||x||y||x||jj 1 yp^lzllg' 


so that 

<r-ij||z||^ i p(z) i p ^IIzIIe 



P = (T^-^^+^l)"^- 




take z = (x.x) e D(A) and set ACx.i) = (Y-Y) « ^' ^ 

(x.i) = A-^^y.y) = (V^Dy-^'i^-y^ ' (2.27) 

Note X = y e V. Now. 

<QAz.z>, = <Q(y.y).A''<y'y>>E 


- Y<y.Av 

= - [llxll'-rdo(x.x) - llxlljl- (2.28) 

From (2.20) then. 

<QA..z>,i-nixn^(reo-i)MillS'^-l'^"^ c^-"' 

, „^ th. theorem follows ft" *^-^" ' 

1 . X n 

(2.26) and (2.29). with f - ^+ 5^ ' 


The Optimal Control Prcbl 


Subsection 3.1 presents some preliminary deflmtm 

-- -^-^^-c ..... _^ : rr:::: r: r ^'^ 

Tb»se results are ge^rio i„ t^ ""' ^'*'='- 

eenerlo In the sense that the Hubert space E l, . 
-rlly the energy space of Section 2 a„. .. " """ 

necessanny represent . °""'°" '' '' ""• ' ^ "« 

y represent en abstract flexible structure as ,n Section 2 r 
second half of th^ ^ section 2. in the 

air the paper, having such generic results will allow us to obt . 
the approxlmatinn t-v.^ ^ °j-j-ww us to obtain 

Ki'i uAxmation theory for fho ^,,f>^ j i. 

y lor the Infinite dimensional stafp ocf . 

analogou. results Tor the control s^bs J "°° "^ 

pr-oDiem. Subsection 3.2 gives <,nm« ^ 
tant ^Plications of the general results for ,. 

iBsuita for the case vhf>r>a *-v,« 
1= that defined In Section 2. °°°"'°' ^='™ 



3.1 The Generic Optimal Regulator Probl 

Let a linear operator A generate a C -^„, 
space E »nH ^ semigroup T(t) on a real Hllbert 

space E. and suppose B a L(r".E). Q c l(E E) . . n m 

^ L(E,E) and R e Kr"), „ith q 

an. selfadjomt and R positive definite and symmetric :.e o tl 

-^^ -.IS to Choose the control u.. ^ ^^ "^ ^^^ ^^ 
functional ' ^ '"''^^ '° "''^"^^ ^^e cost 

"""'" = / (-(t,.Ct,>, . <Ru(t,,u(t,>,m,dt, 
Where the state 2(t) is given by ^^'^^ 

2(t) = T(t)2(0) + Lf. .„ , ^ 

/T(t-T,)Bu(T,)dt,. t 2 0. 


^^^^iflitiaa 1.1. AfunctionueL (o -m . 

S<0.-,U) 13 an MmmiOM ^fintrol ^^ ^ 


initial state z, or simply an admissible control for z, if J(z,u) is finite; 
i.e., if the state z(t) corresponding to the control u(t) and the initial con- 
dition z(0) = z is in L2(0,»;E). 

Definition 3 .2 Let the operators A, B, Q, and R be as defined above. An 
operator n in L(E) is a solution of the Riccati algebraic equation if H maps 
the domain of A into the domain of A* and satisfies the Riccati algebraic 

A*n + HA - nBR~^B*n + q = o. 


Theorem 3 .3 (Theorems 4.6 and 4.11 of [G4]). There exists a nonnegative sel- 
fadjoint solution of the Riccati algebraic equation if and only if. for each 
z e E. there is an admissible control for the initial state z. If n is the 
minimal nonnegative selfadjoint solution of (3.3). then the unique control 
u( ) which minimizes J(z.u) and the corresponding optimal trajectory z( ) are 
given by 

u(t) = -R ^B*IIz(t) 



z(t) = S(t)z, 
where SO is the semigroup generated by A-BR BII. Also, 


J(z,u) = min J(z,v) = <IIz,z>g 

V (3.6) 

If, for each initial state and admissible control. 


llm||z(t)|| = 0, 

there exists at most one nonnegative selfadjoint solution of (3.3). If Q is 
coercive, (3.7) holds for each initial state and admissible control and S( • ) 
is uniformly exponentially stable. D 

We will refer to T(-) as the open - loop semigroup and to S(') as the 
Qptijqal closed-loop semigroup . 

To prepare for the convergence analysis in Sections 5 and 9, we must 
present now some rather arcane estimates for the decay rate of the closed-loop 
system in the optimal control problem. 

Tljsfires 3 .4 Suppose that the open-loop semigroup !(•) satisfies 

llT(t)|I i M^e°^ . t 1 0, 


for positive constants M^ and a^, that H is the minimal nonnegative selfad- 
joint solution to (3.3), and that S(t) is the optimal closed-loop semigroup in 
Theorem 3.3. If there exists a constant F^ such that, for each z e E, 


/ ||S(t)2||^dt i ^^(<^z,z>g + llzll^) 


and a constant ^L such that 

II n II < M^. 

then there exist positive constants Mj and oj, which are functions of J^ , J^. 
M^, and a^ only, such that 


-02 1 

Ils(t)|| < H^e ^ , t 2 0. 


Proof . This follows easily from Theorems 2.2 and 4.7 of [ ]. D 

Lemma 3 .5 . Suppose that there exist positive constants M and a such that 

l|T(t)|| ± Me-'. t 1 0. ^^^^^ 


If z(0) 8 E. h 8 L,(0,»;E). and z(t) = T(t)z(0) + / T(t-3)h(s)ds, then 

/||z(t)||^ dt L 

^l|z(0)|| . ^Ilhll^^ 



Proof . The result follows from (3.12), the convolution theorem [d1, page 95l] 
and the triangle inequality. D 

ieaaa 3 .6. suppose that E is finite dimensional and that the pair (Q,A) is 
observable (in the usual finite dimensional sense). Then there exists a con- 
stant M. which is a function of A, B and Q only, such that 

7||z(t)||^dt L M(/ (<Qz(t),z(t)>g + ||u(t)||^)dt. 


where z(t) is given by (3.2). 

Proof . The proof, which is at most a mild challenge, is based on the fact 
that the observability grammian 


W(t)= / e^ W^dt 

is ooercive for any positive t. □ 


The next theorem says, among other things, that if the open-loop control 
system decouples into a finite dimensional part that is stabilizable (in the 
usual finite dimensional sense) and an infinite dimensional part that is uni- 
formly exponentially stable, then the entire system is uniformly exponentially 
stabilizable, so that (3.3) has a nonnegative self adjoint solution. 

T^eorep) 3 .7. Suppose that there exists a finite dimensional subspace E-^C 
D(A) such that E^ and E^ reduce A (and T(t)), and write 

= r- Li ^ - m- 

Q = 

Qii Qi2 



where A^^ and k^^ are the restrictions of A to E^ and D(A) X Eq, respectively. 


Fll^*^^ ] 
I T22(t)J- 


Also, suppose that the pair (A^^.B^^) is stabilizable and that there exist 
positive constants M^^, a^^ and p such that 

l|T22(t)|| < M^e , t 2 




max{||B||.||Q||} 1 p. ^3 .^^^ 

i) Then there exists F e L(E,R°') sucsh that A-BF generates a uniformly 

exponentially stable semigroup on E. Also. (3.3) has a nonnegative selfad- 

joint solution, and the minimal such solution satisfies (3.10) with M^ a func- 
tion of k^^, B^^, R. M^. a[ and p only. 

ii) If Qj2 = ^"^ ^^^ P^^^ ^"^l' *11^ ^^ observable, then there exists a 
unique nonnegative selfadjoint solution H to (3.3). and there exist positive 

r I 

constants M^ and a^ — which depend on A^^^. B^^, Q^^, R. M^. a^ and p only — 
such that the optimal closed-loop semigroup satisfies 

||S(t)|| 1 M,e"' . t 1 0. ^^_^^^ 

Proof , i) To say that (A^.B-j) is stabilizable means that there exists a 
linear operator F^^ from E^ to r" such that each eigenvalue of A^^-B^jFii has 
negative real part. Hence A-BF generates a uniformly exponentially stable 
semigroup if F = [F^^ 0]. so that there exists an admissible control for each 
initial condition in E. 

It is easy to write down an upper bound for the performance index in 
(3.1) in terms of R. p . l\, a[ and the decay rate of exp( [A^^ -B^^ F^^ It). 
That the H^ in (3.10) depends only on A^^, B^i. R, M^- a^ and p follows then 
from the fact that F^^ is a function of Aj^. and Bj^j. 

ii) Clearly, (3.8) holds with M^ and a^ depending only on Aj^^, B^^, M^. o^ 
and F^^. Therefore, we have (3.8) and (3.10) with the bounds depending only 
on A^j, B^j, 0^1. R. i%. a[ and p . Finally, the existence of an ^^ for 


(3.9) which depends only on these parameters follows from using (3.1) and 
(3.6) In applying Lemma 3.6 to the part of the gystem on E^ and then Lemma 3.5 
to the part of the system on E^. Part 11) of this theorem then follows from 
Theorem 3.4. D 

Bsmsk 3.8 When we say In Theorem 3.4 that M^ and oj are functions of Mq, K^, 
Mj, and a^ only, we mean, for example, that for two optimal control problems 
on different spaces E, with different operators A, B, etc. , if the same con- 
stants Mq, m^, m^, and a^ work in (3.8)-(3.10) for both problems, then the 
same constants K^ and Oj will work in (3.11) for both problans. Similarly, in 
Theorem 3.7 11). as long as E^, A^^. b^^, Q^^, R, M^. o^ and p remain the 
same, the same M^ and Oj will work in (3.20) even if E^, Ajj. B^^ and Q^i 
change. Q 

3.2 Application to Optimal (k>ntrol of Flexible Structures 

For the rest of this section. A^, A^, A, T(t) , B^ and B are the operators 
defined in Section 2 .1 . and E = V X H is the energy space defined there. 

Rfiaacls 3.9. Theorem 3.7 is useful mainly when all but a finite number of 
modes have coercive damping in the open- loop system and the damped and 
undamped parts of the open-loop system remain orthogonal. This is the case, 
for example, with modal damping. The next theorem does not require ortho- 
gonality of the damped and undamped parts of the ^stem, but it does require 
an independent actuator for each undamped mode. The situation of Theorem 3.10 
is typical in aerospace structures: Any elastic component should have some 
structural damping, but rigid-body modes are common; for a structure to be 
controllable, an actuator is required for each rigid-body mode. □ 


Theorem 3 .10- i) Suppose that A^ = b^bJ and that "a^ = Aq + A^ and ^f^ = d^ + 

A are H-coercive, so that there exist positive constants p . r and p such 
that, for all v e V, 

llvllv 1 pIIvIIh- (3,21) 

d^(v.v) 1 pIIvHh, (3,22) 

d^(v.v) L Yllvlly. (3,23) 


max{||B^||.||Q|M|R||) i P (3,24) 

(The V-continuity of d^ implies (3.23).) Then (3.3) has a minimal nonnegative 
selfadjoint solution H. which satisfies (3.10) with l\, a function of p . y 
and P only . 

ii) Suppose that 

<Qz,z>g I pllzllg, z e E. ^3 25) 

Then the optimal closed-loop semigroup satisfies 

||S(t)|| 1 Mi^ ^ ' t 2 0. ^g 2gj 

where Mj and a^ are positive constants depending on p. y and p only. 
proof , i) The suboptimal control 


u(t) = -B^[x(t) + x(t)] 

produces a closed-loop system with exponential decay at least as fast as that 
in Theorem 2.1. The required upper bound in (3.10) follows then from (3.1), 
(3.6) and (3.24) 

ii) In this case, the M^ m (3.9) is p, and Theorem 3.4 yields the result. D 

Now we will consider the structure of the optimal control law in more 
detail. Since n e L(E.E) and E = V X H, we can write 


Do Hi 


where n^, e L(V,V),n, e UE,V),IL, e L(H,H). and n^ and I^ are nonnegative 
and selfadjoint. With z = (x,x). as in Section 2. (3.4) becomes 

u(t) = -R lB*[IlJx(t) + I]2x(t)I. 


Since Bq e l(r'",H), we must have vectors b^g H, li i i m. such that 


BnU = Z b.u 





T_ nin 

" = ^V2 ••• %3 «R 


Also, for h c H, 

B„h = [<b^.h>jj <b2,h>pj ••• <b^.h>H]T. 
Since U^xd) and U^xU) are elements of H. we see from (3.29) 




(3.32) that the components of the optimal a:)ntrol have the feedback form 

Uj^(t) = - <fj|^,x(t)>y - <g^,x(t)>y, i = l....,m, 
where f . e V and g. e H are given by 


f, = i(R-^),jn,b.^ 

J=-'- (3.34a) 

«i = ,V'''^ij°^'j' ' = ' "• 

J--"- (3.34b) 

We call f . and g. functional gains . 


4. The Approximation Schane 

4.1 Approximation of the Open- loop System 

Hypothesis l.J,. There exists a sequence of finite dimensional subspaces V of 
V such that the sequence of orthogonal projections P„ converges V-strongly to 
the identity, where Py^ is the V- projection onto V^^. Also, each V^ is the 
span of n linearly independent vectors e.. 

Since it should cause no confusion, we will omit the subscript n and write 
just e., keeping in mind that the basis vectors may change from one V to 
another, as in most finite element schemes. Also, we will refer to the Hu- 
bert space E^ = Vjj X V^^, which has the same inner product as E = V X H. 

For n 2 1» we approximate x(t) by 

x„(t) = I 5j(t)ej, 

"^ (4.1) 

where $(t) = (^^(t), 52<t), ... 5n<t))^ satisfies 

M"V(t) + D"5(t) + K"5(t) = B"u(t), 


and the mass matrix m", damping matrix d", stiffness matrix k" , and actuator 

influence matrix B^ are given by 


Bg= [<ei,bj>Hl. 
Of course. (4.2) can be written as 

i = aN + B% (4.2') 




n = n"^. i'^'i'^ (4.4) 

A" = 


Ol (4.5) 

n = r I I . b" = 

Throughout this paper, we use the superscript n in the designation of 
matrices in the n^^ approximating syste. and control prohlaa. like 
A" B". M". etc. Hence the superscript n indicates the order of 
approximation - and it never indicates a power of the matrix. By M^. 
we denote the inverse of the mass matrix m". 

4.«„ ir, thP n^^ aDoroximation, we use the 
in the designation of a linear operator in the n appr 

1 « A cinH R are the operators whose matrix 
the subscript n. For example. A^ and B„ are tne ope 

representations are a" and B^ respectively. 


For convergence analyels. ,t la useful to note tnat (4.1) ana (4.2, or 
(4.2') are equivalent to 

''n^t) = A„z„(t) + B„u(t). 

where z = (v y \^ r, . , 

a <^.Xn'' H„. and A„ . L(E„) and B„ . lCb". e„, are the operators 

whoee ..atrlx repreaentatlons are given in (4.5). Al«,. for any real X. 

Is equivalent to 

(^V + in" + ..n, 1 


•^ XD" H- K")a^ = (;,M" + D")pl + m^P^ 



a^ = XqI - 3I 



vJ = ?J. __. ,.i 5 

J^a.e^ and h^ = J^pJe^, ^^1^2. 
(Substituting A" and (4.10) mto (4.7) yields (4.8) and (4.9)). 


Next, we will prepare to invoke the Trotter-Kai-n .on,-.^ 

xrotter-Kato semigroup approximation 

theorem to show how (4.2), (4 2') anrt (a «\ 

v-*.^/. M.2 } and (4.6) approximate (2.1) and (2.15) 

nrat. „e „U1 treat the case in „Mo. .„ i3 ^.^^,„ <,„ ^,^,_^^ ^^^^^^ 
- that A^-o and T„-*„; the general caee ie a etraight-fo^ard extension, .or 
*0 coercive, the ope^loop semigroup generator A is „ dissiPative. Also. 
ror each n. A„ is dissipative on E„. r.. .ain idea here is to project ,.-A,- 


onto V^ in a certain inner product and observe that the result is exactly 
a-P.y, where A„ is the operator on V„ in (4.6) and (4.7). Of course, we 
need only do this for real X,>0. 

For real X>0 then, define an innerprcduct on V by 

<-.->X = ^'<*'*>H ^ ^^O^*'*^ ^ ^*''V (4.11) 

Under the hypotheses in Section 2 on d^, ''r>^ is norm-equivalent to <-.->v. 
For n 11. let P^(X) be the projection of V onto V„ in the inner product 
<-.->j^. Now let h^.hj, e H and note that 

1 \ / .1 


is equivalent to 




2 / ' UM (4.12) 

(::)■ -iiMSl 

With A~^ from (2.10). (4.13) is equivalent to 

(I . ^-\ . X^A-^)vl . (,,-1 . A-lA,)hl - A-lh^ ^^^ 





1 J- 

v^ = Xv - h . (4.15) 

^n = ^n^*-^^^ ^""^ ''n = ^n^^^^ ' (4.16) 

it follows from (4.11) and (4.14) that 


= <ei.(>.^A-l+XA-\+l)vSy 

= <ei.(XAolM-^Ajj)hl+A-lh2>y. 



and from (4.15) that 

2s _ .. 2 

^^i'Vx = <«i'V >x = >^<ei'V^>x - <ei,h^ 

X • 


NOW. for h^ = hj s V„. h^ = hj e v„, ,„d vj. v^„. hj and h^^ written as in 
(4.10). (4.17) and (4.18) yield (4.8) and (4.9) again. 

This shows that 

rp„(x) 1 

I P„(X)J<^-A)"'|e„ = <^-An)"'. 


which yields 

I P^(X) J<^-A> 

^En = <^-An>"'PEn 


where P^^ ig j^ E-^jiQififiLLfiD of E .aitfi E„. The projection P^^ can be written 

_ Pvn 1 
L PHnJ ' 


where P^^ is the V-projection onto V^. as before, and Pj^^ is the H-projection 
onto V^. Since the V-norm is stronger than the H-norm and the norm induced by 


the X-lnnerproduct is equivalent to the V-norm. it follows from Hypothesis 4.1 
that (>.-A^)~ Pg^ converges E- strongly to (X-A)~^ as n-» «. Now, with A 
extended to E^ as, say. n(Pg^-I) , Trotter-Kato [Kl, page 504, Theorem 2.16] 
yields the following. 

Theorem 1.2. For Aq coercive, let T^ C) be the (contraction) semigroup gen- 
erated on E^ by A^. Then, for each t 2 0, T^(t)?^^ converges strongly to 
T(t), uniformly in t for t in bounded intervals. 

In the general case, when A^ is not coercive, the open-loop generator A 
is obtained from the dissipative t by the bounded perturbation in (2.13), so 
that [G3, Theorem 6.6] yields the following generalization of Theorem 4.2. 

gorolAarv 4.3. Let T^C) be the semigroup generated on E by A . Then, for 
each t 2 0, T^(t )Pg^ converges strongly to T(t), uniformly in t for t in 
bounded intervals. 

^^'^^^W l-l. When A has compact resolvent, (Ji-A^) ^Pg^ converges in L(E) t< 

ICfifif. This follows from (4.20) and a standard result that the projections of 
a compact linear operator onto a sequence of subspaces converge in norm if the 
projections converge strongly to the identity, as do P and P (X) . D 

That the adjoint semigroups also converge strongly follows from an argu- 
ment entirely analogous to the proof of Theorem 4.2. In particular, equations 
like (4.11)-(4.17) are used to show that 


I p„a)j^^-^^ 

^En = <^-<^'''En 


In showing this. A'* is used as A"^ was used above. Also, care must be taken 
to calculate A* with respect to the E-inner product. The result is 

Theorem 4.5. Let T (') be the sequence of semigroups in Corollary 4.3. Then, 
for each t 1 0, T*(t)Pg^ converges strongly to T*(t). uniformly in t for t in 
bounded intervals. 

Finally, for the approximation to the actuator influence operator B e 
L(R°,E), recall B e L(r'"» \) . the operator whose matrix representation is 
the matrix b" in (4.5). From (4.3), it follows that 

^n " ^En^- (4.23) 

Since B has finite rank m. B^ and B* converge in norm to B and B . respec- 


4.2 The Approximating Optimal Control Problems 

The n^h optimal control problem is: given z^(0) = (x„(0) .x„(0)) e E^, 
choose u e ^(O.-;!^) to minimize 

J (z„(0).u) = J«Q„z„(t).z„(t)>, . <Ru(t).u(t)>Rm)dt. ^^ 


where Q^= PEnQlEn* ^^ ^^^""^^ 

H,,,,,^ 4.6. For each n > 1 and z„(0)e E„. there exists an admissible con- 
trol (Definition 3.1) for (4.6) and (4.24). D 

.-.■ rr.. Hvnothesis 4.6 is that, for each n. the system 
A sufficient condition for Hypothesis 't.o 

(A ,B ) be stabilizable. 

By Theorem 

3.1. the optimal control u^(t) has the feedback form 

u^(t) = -R'^^Dh^n^t) (4.25) 

4- . nn F TT i3 nonnegative and selfadjoint. and !!„ 
where n^ is a linear operator on E^. IJ^ i3 nonn«6 

satisfies the Riccati equation 

aX - Dn^n - nnBn«''<°r. ^ ^n = «• (4.26) 

. A ^ (A m) ha' at least one nonnegative. selfad- 
As a result of Hypothesis 4.6. (4.26) ha. at xe 

,oint solution. The minimal such solution is the correct H, here. If the 
system (A^,Q„) is observable, then II„ is the unique nonnegative. selfadjoint 
solution to (4.26). and is positive deflate. If we write D, as 


\ = 

non nin 

then (4.25) becomes 


The feedback law (4.28) can be written in functi 
as in Section 3. We have 


onal-feedback form, just 


"n(t) = [u,„(t) U2„(t)...u^^(t)]T. 



"in^t) = - <fin,x„(t)>y - <6^^.;^(t)>j^, 1 < i i „. 



j?i^^ ^j^lnPHnbj' H i 1 m. 



T (R^) 

jll'' 'ijI^nPHn^j - 1 < i i m. 



Of course, f^^ ^nd g^^ are the n^^ approximations to the functional gains f . 
and gi in (3.34). "" 

In Section 5, (4.25)-(4.31) will be useful for studying how t^e solution 
to the nth ,p,,^^ ^^^^^^^ ^^^^^^ ^^^^^^^^^ ^^ ^^ ^^^^^_^^ ^^ ^^ ^^^^^^ 

probl^ Of section 3, but for numerical solution of the nt^ p.^blan, we need 
the matrix representations of Uiese equations. 

We will need the following grammian matrices: 


K" = [<e^,ej>v] = k" + t<Aiei.ej>H] 



.0 m"j 


Note : The matrix W~" will be the Inverse of W"^. The superscript n on any matrix 

indicates the order of approximation, not a power of the matrix. Also, recall 
the note following (4.5). 

Now recall Q^ = PEnQlEn* ^^'^°® Q = Q* e L(E) and E = V X H, we can write 

Q = 

Qo Qi 


where Q^ = Q^e L(V) , Q^ e L(H,V), and 02=02^ L(H) 
lation shows that 

Straightforward calcu- 


where Q" is the matrix representation of Q and q" is the nonnegative, sym- 
metric matrix 







°1 = [<ei,Qiej>v], 


QJ = [<e^.Q2ej>H]. 

Also, recall that A and B are the operators whose matrix representations are 
given by (4.5), and note that the matrix representations of A^ and B^ are 
W""(A")'^W" and (B")%", respectively. 

With the matrix representation of II denoted by H"* the Riccati operator 
equation (4.26) is equivalent to the Riccati matrix equation 

W""(A")%"II" + IPA" -I1"B"R"^(B")'^W"IP + Q" = 0. 


While n is selfadjoint, D" in general is not symmetric, but the matrix 

^ = „njjn 


is symmetric and nonnegative, and positive definite if H^ is. Premulti plying 
(4.39) by W", we obtain 

(A")''^n" + &^A" - frB"R"l(B")'^n" + Q" = 0. 


which is the Riccati matrix equation to be solved numerically. 

Now we need one more set of matrix equations for the numerical solution 
of the n^*^ optimal control problan. Since the functional gains f^^ and g^^ 
are elements of V^, they can be written as 

n f^ n g^ 

^in = .^, Pj ^j ^^ 8in = .^ Pj ^j' i = 1. •••.«>. 

J-1 J"-^ (4.41) 

f . e . f • g • *•* 

where p ■"■, p ■"■ e R". We need equations for p ^ and p ■"" in terms of IF. One 


way to get these equations is to partition lP(obtained from (4.39)) and then 
work out the matrix representation of (4.31), However, another approach is 
more instructive because it relates the present Hllbert space methods to the 
standard finite dimensional solution of the n^^ optimal control problan. 


The n optimal control problem can be stated equivalently as: giv 
Tl(0) = [5(0)T.^(0)T] e R2n ^^^^^ ^ ^ L_(0.a;Ri°) to 



'^^(r\(0).u) = y(T,(t)^"T,(t) + u(t)'rRu(t))dt, 


where Ti(t) = [4(t)^Ut)'^]^ satisfies (4.2'). For (4.2') and (4.42), the 
optimal control law is 


"n = -^ ^B"D"Ti(t) 


where IP is the minimal nonnegative, symmetric solution to (4.40). 


Since § is related to x^ by (4.1), the optimal control u„ in (4.43) must 
be equal to the optimal control u^ m (4.29) -(4.31) . Substituting (4.41) into 

(4.30) yields 


- (p ')^"5 - (/^)M = -[(pSt (^^i)T]w^, 


Then, using (4.44) and equating (4.29) and (4.43) yiel 


^1 ^2 
P P 



i,"^ ,''■■■ f«" 

,-n TinnnD-1 

= w""^ IFb'^r 


W§ iiow Mve ihe complete ^plMtjon to the n^^ optimal control problem : 


The Riccatl matrix equation (4.40) is solved for 6^; then the optimal control 
is given by (4.43). and equivalently by (4.29)-(4.30) with the functional 
gains f,„ and g^^ given by (4.41) and (4.45). In the next section, we will 
give sufficient conditions for the solution to the n^^ optimal control problem 

to converge to the solution to the optimal control problem in Section 3 for 

the original infinite dimensional system. 


5. Convergence 

As in Section 3. subsection 5.1 will state some results for the optimal 
linear regulator problem involving generic linear operators A, B. Q, etc.. on 
an arbitrary real Hilbert space E, and subsection 5.2 will expand upon these 
results for the particular class of control problems treated in this paper. 

5.1 Generic Approximation Results 

Let the Hilbert space E and the linear operators A. KO, B. Q and R be 
as in Section 3. Suppose that there 1. a sequence of finite dimensional sub- 
spaces E^. with the projection of E onto E„ denoted by ?£„• such that Pe^ con- 
verges strongly to the identity as n ^- » , and suppose that there exist 
sequences of operators A^ e L(E„) . B^ . L(R°.E^). Q„ = Q* e L(E^). Q^ 1 0. 
such that we have the following strong convergence. For all z e E and t^O, 

exp(Aj^t)PEnZ "> T(t)z (5,1) 


exp(A*t)Pg z -> T*(t)z (5^2) 

as n -> -. uniformly in t for t in bounded intervals; for each u e R . 

BjjU -» Bu; (5.3) 

for each z e E, 

°nW "^ ^- (5.4) 

IllS,^i.l. Suppose that for each n there is a nonnegative. selfadjoint 
linear operator D^ on E„ which satisfies the Riccati algebraic equation 

If there exist positive constants M and p. independent of n. such that 

llexp([A^-B^R-Vl]j^]t)|| < Me"Pt. t 2 0, 

and if II n^ii is bounded uniformly in n. then the Riccati algebraic equation 
(3.3) has a nonnegative selfadjoint solution H^ and, for each z e E. 





z -» S(t)z 

uniformly in t 2 0, where SC) is the semigroup generated by A-BR-lfi^ . 
there exists a positive constant 6, independent of n, such that 



then II u^ii being bounded uniformly in n guarantees the existence of positive 
constants M and p for which (5.6) holds for all n. 

^Tfiof. The theorem follows from Theorem 5.3 of [G4] when the operators A . Q 
andn^ are extended to all of E by defining them appropriately on Ej^. For the 
details of this procedure, see Section 4 of [Gl] . Or better. Banks and Kun- 
isch [B6] have modified Theorem 5.3 of [G4] to obtain essentially the present 
theorem without using the artificial, and rather clumsy, extensions to E"^ m 
the proof. D 


IS.^ 1.1 I.e .tron, oonvergenoe in ,5.7, Implies u„lfo„ „o™ convergence 
Of the optimal feedback laws: 

"^nVE_ - B*ni| -^ 


as n 


to^r. r.U r„uc«3 ..„. tne seara^„.„t„e.. c.1^ ,„, ,^^ ,„, ,,^ ,^^^^ 

di^enaicnallty of the control space r". See e,^Uo„3 (4.23, an. <4.24, of 
[Gl]. D 

^^mrss 5.3 Assume the hypotheses of Theorem 5.1 but do not assume (5.6, or 
<^-^>- If II n„|| is bounded uniformly m n. then the Riccatl algebraic 
equation (3.3) has a nonnegative selfadjoint solution H. and, for each z e E. 
HjjPgjjZ converges weakly to Hz. 

imf. n,U 13 ^eo.» .., or r03,, Whose proof .s valla under the hypotheae. 
here. □ 

I^e mam shortcoming of the weak convergence in I^eorem 5.3 is that it does 
not yield uniform norm convergence of the feedback control laws. 


5.2 Convergence of the Approximating Optimal Control Problems of 

Section 4 .2 

For U.e rest of this section, A^, A^, A. T(t) . Bj, and B sre the operators 
defined In Section 2. The operators A„. B„, a„ and n„ are the operators In 
the approximation sohane of Seotlon 4. In particular. D . L(E„,E„) Is the 
.inlmal nonnesatlve. self-adJolnt solution of the Hlccatl operator e-uatlon 
(4.2«). According to Corollary 4.3 and Theorem 4.5. the Eltz-Galerl^n approx- 
imation sch»e presented In Section 4.1 converges as required In (5.1) and 
(5 2): (5.3) and (5.4) follow froc (4.23) and the definition Q„ - Pg„QlE„ m 
Seltlln 4.2. Also, i^pothesls 4.6 guarantees for each n the existence of the 
required solution of the Riccati equation (5.5) in Theory 5.1. 

Since II„ is nonnegative and self-.djolnt. its eigenvalues, which are 
also the eigenvalues of its Matrix representation, are real and nonnegative. 
and its norm is equal to its maximum eigenvalue. 

THeorem 5.1 If Is E-coerdve and a„ - (i.e.. there is no open-loop damp- 
ing), then there is no nonnegative self-adjoint solution of the Eiccti opera- 
tor equation (3.3), and 

IIU^II ^ « as n -» -. (5.11) 

,^. Recall the operator t in Section 2.1. By Theory 1 of 102, , there can 
he no com;«ct operator C . L(E.E) such that t . C generates a uniformly 
exponentially stable semigroup. Therefore, since a compact linear perturba- 
tion Of r yields A, there can he no com^ct linear C such that A . C generates 
a uniformly exponentially stable semigroup. 


Now, unless (5.11) holds, there exists a subsequence such that || JT || 


is bounded in n., so that Theorem 5.3 says that there exists a nonnegative 
self-adjoint solution 11 of (3.3). Since Q is coercive. Theorem 3.3 then 
says that the semigroup generated by A - BR~^B*n is uniformly exponentially 
stable. But this is impossible -- BR~-'-B* 11 Is compact because its rank is not 
greater than m. D 

Theorem 5.5. Suppose that Aq and dQC.*) are both H-coercive. Then there 
exist positive constants Mj^ and a^, independent of n, such that 

llexpEA^tlll i M,e"^ , t 1 0. ^^_^^^ 

Proof . First, we define A^^ and D^^^ e L(V^,V^) to be the operators whose 
matrix representations are M""k" and M'^d", respectively. (See Section 4.1.) 
The operator A is then 

'^ ^ [ -\n -^On| 


Since Aq and dQ are H-coercive, there exists a positive constant p. 
independent of n, such that 

<Ao„h.h>j, 1 pllhll^ ^^ .^^j 


<Do„h.h>H I Pllhllj ^^^^5j 

for all h e V . Since dn Is continuous on V X V, there exists a positive 
n w 


constant y. Independent of n, such that 

<Donh.h>H i Yl|h||J 


for all h c V^. The theorem follows then from Theorem 2.1. O 

TiiSOrsB 1'6. Suppose that A^ has an invariant subspace V^ which is also 
invariant under the damping map A~ A^ , that E^ = V^ X Vq is a stabilizable 
subspace for the control system, and that the restrictions of A_ and 6A»,») 
to Vq are both H-coercive. Also, suppose that Vq has finite dimension nQ and 
that, for each n 1 n^ in the approximation scheme, the first n^ e. , span V» 
and the rest are orthogonal to V^ in both V and H. 

i) Then (3.3) has a nonnegative solution 11, and for each n 2 n^., (5.5) has a 
nonnegative self-adjoint solution!^. Also, I^ is bounded uniformly in n, so 
that H^ converges to II weakly, as in Theorem 5.3. 

ii) If Eq and E^ (the E-orthogonal complement of Eq) are invariant under Q, 

and if the part of the open-loop system on E. is observable with the measur 


ment Qz, then (5.6)-(5.8) hold as in Theorem 5.1. 

Froof. We will invoke Theorem 3.7 to establish the existence of the uniform 

bounds and decay rates needed in Theorem 5.1. In the approximating optimal 

control problems, the part of the control systm on E. is the same for each n; 

the approximation of the control system takes place on E^; We can write E = 

" n 

Eq © E^n' where Eq^ Is the orthogonal complement of Eq in E^, and Eq and E^ 
clearly reduce the open- loop semigroup for each n 2 n^j. 

For the part of the open- loop system on E^^, Theorem 5.5 establishes 


positive Mj^ and a.. Independent of n, for (3.17). Also, we have a ^ 
independent of n for (3.18) because B^^ = Pg^B and Q^^ = PEn*^'En* Therefore, i) 
follows from Theoran 3.7 i) and Theorem 5.3. 

The definition of Q and the requirement on where the various basis vec- 


tors must lie imply that E^ and E^^ reduce Q^ if Eq and Eq reduce Q and that 
the restriction of Q to Eq is the same for all n. Therefore, ii) follows from 
Theorem 3.7 ii) and Theorem 5.1. D 

Remark 5.7. In applications, the subspace V^ in Theoron 5.6 usually contains 
rigid-body modes. The theorem includes the case where both A^ and dQ are H- 
coercive on all of V (no rigid-body modes and all modes damped). In this 
case, Vq is the trivial subspace. D 

Remark 5.8. Otherwise, for applications to flexible structures. Theorem 5.6 
usually requires two things: first, modal damping must be modeled for the 
structure, so that the natural modes remain uncoupled in the open-loop system; 
second, the natural mode shapes must be used for the basis functions in the 
approximating optimal control problems. Although these requirements may seem 
restrictive from a mathematical standpoint, such modeling and approximation 
predominate in engineering practice. Also, we get our strongest convergence 
results under these conditions. For applications where the basis vectors are 
not the natural mode shapes, the following theorem is useful. D 

Theorem 5.9. Suppose that Aq + BqBq and d^ + BqBq are H-coercive. Then (3.3) 
has nonnegative solution II, for each n (5.5) has a nonnegative self-adjoint 
solution n, and || II 1 1 is bounded uniformly in n. Hence Theorem 5.3 
applies. Furthermore, if Q is E-coercive, then (5.6)-(5.9) hold in Theorem 5.1, 


Proof . The required bounds follow from Theorem 3.10 and the proof of Theorem 
5.5. Although we took A = b«B„ in Theorem 3.10, this is not necessary in the 
final result, since all bounded self-adjoint operators A^^ on H that make Aq + 
k^ coercive yield equivalent norms for V. D 

Theorem 5.10. If (5.7) holds for each z e E, then 
"l^in - ^illy-^^' 

"^in ~ Silln -^0' as n -» 



where f^ and g^ are the functional gains in (3.16), and f^^ and g,^^ are the 
approximating functional gains in (4.31) and (4.41). 

Proof . The result follows from (4.31). □ 

Note that (5.10) and (5.17) are equivalent. 


6 . Exam pi e 

6.1 The Control System 

One end of the uniform Euler- Bernoulli beam in Figure 6.1 is attached 
rigidly (cantilevered) to a rigid hub (disc) which is free to rotate about its 
center, point 0, which is fixed. Also, a point mass m, ^3 attached to the 
other end of the beam. The control is a torque u applied to the disc, and all 
motion is in the plane. 

Figure 6,1. Control System 


10 in 
r = hub radius 

. 100 in 

I = beam length 

In = hub moment of inertia about axis 

perpendicular to page through l^O slug in 

m. = beam mass per unit length 

m, = tip mass 

EI = product of elastic modulus and second moment 

13,333 slg in^/sec^ 
of cross section for beam -^^ ' '^ 

fundamental frequency of undamped structure -9672 rad/sec 

Table 6.1 Structural Data 

.01 slug/in 
1 slug 

The angle represents the rotation of the disc (the rigid-body mode), 
w(t,s) is the elastic deflection of the beam from the rigid-body position, and 
w^(t) is the displacement of m^ from the rigid-body position. For technical 
reasons, we do not yet impose the condition w^Ct) = w(t,|); more on this 

l^e control problm is to stabilize rigid-body motions and linear (small) 
transverse elastic vibrations about the state = and w = 0. Our linear 
model assumes not only that the elastic deflection of the beam is linear but 
also that the axial inertial force produced by the rigid-body angular velocity 
has negligible effect on the bending stiffness of the beam. The rigid-body 
angle need not be small. 

For this example, it 'is a straight forward exercise to derive the three 
coupled differential equations of motion in 0. w and w^ , and they do have the 


form (2.1'). However, to «nf*iasize the fact that we do not use the explicit 
partial differential equations, we will not write these equations here. 
Rather, we will write only what is normally needed in applications: the 
kinetic and strain-energy functionals, the damping functional and the actuator 
influence operator. 

Remark 2.1 applies to this example, and to most examples with complex 
structures. The generalized displacement vector is 

X = («.w,w^) e Hq = R L2(0,|)X R. 
The kinetic energy in the system is 

Kinetic Energy = V2 <x.x>^ 
where H is H^ with the inner product 

<x,x>jj = m^ /q [w+(r+s)e] [w+(r+s)e]ds 




+ loOS + mi[wi+(r+J?)«][(*i+(r+i)^]. 

As in most applications, we need not write the mass operator explicitly, but 
there exists a unique selfadjoint linear operator ^L on H^ such that 

<x,x>jj = <MqX,^>jj . 

It is easy to see that M^ is bounded and coercive. Hence Hq and H have 
equivalent norms. 

The input operator for (2.1') (which maps R to H-) is 


Bq = (1,0.0). (g5j 

Since we multiply (2.1') by li^^ to get (2.1). the input operator for (2.1) is 
(Mq'^Bq) . Note that 

(Mq Bq) = Bq, ^g gj 

where (Mq^Bq)*" is the H-adjoint of (Mq^B^) and B* is the HQ-adjoint of Bq. 

Remark 2.2 also applies here. The only strain energy is in the beam and 
is given by 

Strain Energy = Vi a(x,x) 


a(x,x) = EI/q w"w"ds. 

where o" = d^O/ds^C). To make aC,*) into an inner product, we must 
account for rigid-body rotation. Thus we set 

<x,x>y = a(x,x) + eS ^^^^j 

and define 

V = {x = (0.0.0(1)): e H^(O.I), 0(0) = 0'(O) = }. 


Also, we have 

<x.x>„ = a(x,x) + <BqB!x,^>jj 


= a(x.x) + <(Mq1Bq)(Mo^Bq)*^x.x>jj. 


so that Aj = BqB!, or (Mq^B^) (M^^B^)*^, depending on whether the H^ or the H- 
inner product is used in computing the V- inner product. But jje need Qejther A^^ 
nor Aq explicitly . W§ need only (S..8) and (i.i) » along with (6.3) . io QQn^PUte 
iM required inner pro(avtcta. 

As mentioned in Remark 2.2, the operator A. can be defined now by (2.6), 
and the stiffness operator is A^ = A^ - A^^. Using the H^-inner product in 
(2.6) yields the A. for (2.1'), and using the H- inner product yields the Aq 
for (2.1), which is I^^ following the A^ for (2.1'). The Aq for (2.1') is 
quite simple, and the reader might write it cut. We will not, so that no one 
will think that we use it. We will point out that D(Aq) requires both the 
geometric boundary conditions in V and the natural boundary condition w''(t,X) 
= 0; i.e., zero moment on the ri^t end. 

Remark 6.1. That the geometric boundary conditions 

w(t,0) = w'(t,0) = 



w(t,Jf) = w. (t) 

^ (6.13) 

are imposed in V but not in H — i.e., on the generalized displacement but not 

on the generalized yelocity — is common in distributed models of flexible 

structures. The natural norm for expressing the kinetic energy of distributed 

components is the L.-norm, which cannot preserye constraints on sets of zero 

measure. Because the strain energy inyolves spacial derivatives, the stronger 

strain-energy norm can preserve the geometric boundary conditions (although, 

as for the boundary slope of an elastic plate, the V-norm may impose some of 


these boundary conditions in an L^ rather than a pointwise sense). The 
strain-energy norm is based on the material model of the distributed com- 
ponents of the system, and it should not be surprising that such a norm is 
required to connect the various structural components. D 

We assume that the beam has Voigt-Kelvin viscoelastic damping [C2] , so 
that the damping operator in (2.1) is 

"» " "O*" (6.14) 

where Cq is a constant. This means that the damping functional is 

djj(x,x) = Cfj a(x,x), x,x e V. 


6.2 The Optimal Control Problem 

We take Q = I in the performance index in (3.1). This means that the 
state weighting term <Qz,z>g is twice the total energy in the structure plus 
the square of the rigid-body rotation. Since there is one input, the control 
weighting R is a scalar. 

According to (3.33), the optimal control has the feedback form 

u(t) = - <f,x(t)>„ - <g,x(t)>„ 

" (6.16) 

where x(t) has the form (6.1), and 

f = (a-.,^ = R~^n,Bo 8 V, 



& = (a_.(« .ft ^ = o~l 

g»0g.Pg) = R n^B^ 8 H. 

that p^ = 0^(;) i3 „^t used in the control law-recaii (6.8) and (6.9). 

6.3 Approximation 

Our approximation of the distributed model of the structure Is based on a 
finite elonent approximation of the beam which uses Hermlte cubic splines as 
basis functions ([S1.S4]). These are the basis functions most commonly used in 
engineering finite elanent approximations of beams, l^e splines and their 
first derivatives are continuous at the nodes. Because the basis vectors e 
m the approximation scheme in Section 4 must be in the space V defined in ^ 
(6.10), we write them as 

©1 = (1.0,0). 


^j = (O.0j,0j(l)). j = 2. 3. . . ., 

Where the .^'s are the cubic splines. When we use n^ el«nents to approximate 
the beam, there are 2n^ linearly Independent splines. Ihus. with the rigid- 
body mode, the order of approximation is n = 2n + i 

For the numerical solution to the optimal control problem, we have only 
to Plug into the formulas of Section 4. T^e matrices in (4.3) are calculated 
according to (6.3). (6.8) and (6.P) , with B, g,,en by (6.5). m particular. 

K" = [a(e,,ej)]. d'^ = e^ k". m" = l<e,,e^>^U 


BJ - [10 - 01^ . l<e^,,^l,,,o.on^, . I, ,,,„_,, 



Hote that the first rc» an. col>-n of K" are zero. Ihe .atrlx k" 1. (4.32) is 
K" »lth 1 added to the first el^ent. ^e .atrloes A" and b" are given by 
,4.5) and. since Q - I. the .atrix fl" is the w" in (4.33). With the^ 
.atnices. we solve the Biocati equation (4.40) and use (4.41) and (4.45) to 
compute the approximations to the functional gains, which are 

fn " '"fn-'fn'^fn'' (6.21a) 

«n " <VV'V' (6.21b) 

For convergence, we satisfy all the hypotheses of Theorem 5.9. In par- 
ticular. Since Q is the identity on E. it is coercive. Theorem 5.9 implies 
that the solutions to the finite dimensional Rlccatl equations converge as in 
Theory 5.1 and that the functional control gains converge as in theory 5.10. 

as,^ £.2. It might appear that the hypotheses of Theorem 5.6 hold with n„ - 
1, but not so. For J JL 2, e^ is orthogonal to e, in H„ and V not in H. 
Recall («.l)-<6.3), (6.9)-(6.11) and (6.18). □ 


6.4 Numerical Results 

Figures ..2a and «.2b show the computed funotloml gain kernale ,;; and 
<g„ for the damping coefficient c,, - lo"". the control weighting R . l^^'and 
"e = 2. 3. 4, 5 and 8 beam elanents. Table 6.2 Usta the corresponding scalar 
oom^nents of the gains, for c„ . i„-^ and B - 05. the convergence Is slc«er 
as discussed bel». To shew the complete story of convergence. Flares 6.,a 

and «.3b and Table «.3 show the results f or n . 2 3 4 5 , ^ ^, 

e -^.3.4.5,8, and Figures 6.4a 
and 6.4b and Table 6.4 show the results for n^ . 4.6,8,10. 

We have plotted ,;; because the second derivative appears in the 
stral^^energy inner product in C6.8) and (6.9, and ,,^ converges in H^(0,|,. 
Note that. Since the Hermite cubic splines have discontinuous second deriva- 
tives at ^e nodes, the appro>cimations to ,;' are discontinuous at the nodes. 

Although H -convergence guarantees onlv i ' ' 

6 arancees only L2-convergence for 0^^, it can be 

shov^n that ^;; converges uniformly on [oj] for this problem. 

l^e tables omit p^^ to emphasize the fact that it does not appear in the 
feedback law and the fact that the ^nvergence of ,^^ ,3 not an independent 
Piece Of information about the convergence of the control gains; since ,,„(o, 
= 0f.„(O) = 0, the convergence of ^'/^ i„.pUes the convergence of p^^ = ^^^"(j^, . 

On the other hand, although B ~ a tC\ ^ 

""^ "gn - «gn <X' for each n, the H-norm convergence 

Of g„ does not enforce this condition in the, as the V-norm convergence 
Of f„ enforces f^ . ,^<|,. Hence, as far as we can tell fro. our results in 
sections 3.5, f,„ is an independent indicator of the convergence of the con- 
trol gains, as well as being used in the control la. in <6.16,. However, the 
behavior of ,^ ,„ p,^,,, ,.,,, ,.3, ^„^ ^„^ ^^^^^^ ^^^^ ^^ ^^^^^^ ^^ 

V. Stronger results on the continuity of «^ and the convergence of » (jf, 


3H0U1. rouo. rrc a U>eo.» .taU„« that. ^oau.e t.e op..loop =e..s.oup 
generator A is a^lytic. the solution to the infinite ai.ensional Hicoati 
^uation »aps ail or E into .CA*, . -e Tact that .^ (i- converges to .e.c in 
Pigure 6.2a also suggests such a the<.». but we have not proved it. 

4 K„„. filed the two factors that detennlne the rate 
With the state weighting Q fixed, me 

Of convergence are c„ and R. Althou* we have used splines to apprcxiMSte the 
^a., the relation between the convergence rate and c„ and B prohabl, can he 
i„.er.eted best in ter.s of the „-ber of .tural .odes of the structure that 
,he optical infinite di.ensio^l controller reall, controls. Strictl, speak- 
ing, the controller controls all .odes, but the functional gains lie essen- 

.r n,» mina if we used the natural modes as 
of modes required for convergence of the gains 

the tasis vectors in the approximation, ^e rest of the modes are practically 
<hut not exactly, orthogonal to the functic^l gains, so that the optimal 

4-v.-n Tn eeneral, the lighter the damping, 
feedback law essentially ignores them. In general, 

H . th.t will be controlled for given Q and R; the cheaper the con- 
the more modes that wiii oe wun wj. 

. ^v,of will be controlled for given Q and c^. The question 
trol. the more modes that will oe conw^j-x 

of the convergence of the finite client approximation to the functional ^ins 

hecomes then a duestion of h«, ma. .odes the optimal control law really wants 

i«,oni-<» ^t takes to approximate those modes, 
and how many elements it caKes i,u ai,^ 

Hv-erical experience with optimal octroi of flexible structures has 
ehown this modal interpretation of the convergence of the approximating con- 
trol laws to be very useful, and that it is difficult to improve upon the 
^tural modes as basis vectors for the approximaUon scheme (see [G5U H»- 
ever. whether the natural modes are always or almost always the hest basis 


vectors Is an open question. We use the cubic splines here to demonstrate 
that a standard finite element approximation works quite well. Also, to use 
the natural modes as basis vectors here, we first would have to compute them 
using a finite element approximation — as in most real problems ~ and we do 
not know in advance which or how many modes are needed. On the other hand, if 
the most important natural modes are determined from experiment, then modal 
approximation should be best. 


lb. DO 2b. 00 

Figure 6.2a. Functional Control Gain Component if.^" 
Damping coefficient Cj, = lo"^; control weighting R = 1 

b.oo 3b. 00 3b. 00 lib. 00 sb.oo 

Elgucfi 6. lb. Functional Control Garln Component i^ 
Damping coefficient c^ = 10~^; control weighting R = 1 

number of elements n 

2, 3, 4. 5. 8 

number elements n 

2. 3. 4. 5, 8 


EigjlCfi 6.1a. Functional Control Gain Component ^f„ 
Damping coefficient c^ = 10-^ control weighting R 
number of elements n^ = 2, 3, 4, 5, 8 

■ o.oo 

Figure 6.3Ji. 

Functional Control Gain Component i^ 


Damping coefficient c^ 
number of elements n^ = 

, lO""*; control weighting R 
2, 3. 4. 5. 8 



£iKUO 6.45. Functional Control Gain Component ^^ V. 

Damping coefficient o - in-<. 

""^ Oq - iO ; control weighting R = .05 

number of elements n = 4 g „ ,„ 
e ^ » " . o , 10 

£i«Ui:S i.ii,. Functional Control Gain Component , 
Damping coefficient c, . io-^• control weighting hI .05 
number of elonents n^ = 4. 6, 8. lo 

°fn °gn Pgn 

2 1.000 .2141 -22.459 

3 1.000 .2396 -25.221 

4 1.000 .2496 -26.331 

5 1.000 .2534 -26.786 
8 1.000 .2561 -27.041 

lafele 6.2. Scalar Components of Functional Control Gains 
Damping coefficient c^ = 10"^ control weighting R = 1 
number of elements n = 2, 3, 4, 5, 8 









T£^l?le 6 .3 . 

Scalar C 

omponents o 

f FiinnHni 


Damping coefficient c^ = lo"^ control weighting R = .05 
number of elements n = 2, 3, 4, 5, 8 













T^bl9 6.4. 

Scalar C 


if Piinof 

^ nr 

Damping coefficient c^ = 10"^ control weighting R = . 

ng R = .05 

number of elements n = 4, 6, 8, 10 


Figures 6.5a and 6.5b and Table 6.5 represent attempts to compute an 

optimal control law for the structure when R = .05 but c^, = 0. Since Q is the 

identity operator in E and hence coercive. Theorem 5.4 says that no optimal 

control law exists and that the norm of the solution to the finite dimensional 

Riccati equation grows without bound as the number of elements increases. 

This is reflected in the nonconvergence of a^^. i>^ and Pg„. although a^^ con- 

f f 
verges and the convergence of 1>^^ is unclear. 

In applications where the structural damping is not known, except that it 
is very light, it is tempting and not uncommon engineering practice to assume 
zero damping in the design of a control law for the first few modes, while 
trusting Whatever damping is in the higher modes to take care of them. How- 
ever, if high performance requiranents (large Q) or coupling between modes in 
the closed-loop system necessitate a control law based on a more accurate 
approximation of the structure. Theorau 5.4 and the current example warn that 
the higher-order control laws are likely meaningless and rather strange if no 
damping is modeled. 

We should note that we have seen similar probl«ns [G9] where 11^ remains 
bounded and the gains converge for zero damping but finite-rank Q. In such 
cases. Theorem 5.3 says that an optimal control law exists for the distributed 
model of the structure and that the finite dimensional control laws converge 
to an optimal infinite dimensional control law. Also. Balakrishnan [B2] has 
shown that an infinite dimensional optimal control law exists for no damping 
when Q = BB*. 


15:^S iSToo sJToo irtToo iOo sToo jSToo SHo iSIoT 

Figure 6.5a. Functional Control Gain Component 0f„" 
Zero damping; control weighting R •= .05 

lb. 00 ilToO 30.00 «b.00 SO. 00 sb.aO 70.1 

Figure 6.5t?. Functional Control Gain Component ^ 
Zero damping; control weighting R = .05 

•b.oo iSTi 



number of elements n = 2. 3, 4, 5, 8 


number of elements n = 2, 3, 4, 5, 8 








-112 .23 

















iMhlS 6.5. Scalar Components of Functional Control Gains 
Zero damping; control weighting R = .05 
number of elements n = 2, 3, 4, 5, 8 


7 . The Optimal Infinite Dimensional Estimato r. Compengator and Gigged - Ififija 

As in Sections 3 and 5, we will state sc-me initial definitions and 
results for an arbitrary linear control system on a Hilbert space in Subsec- 
tion 7.1, and then discuss implications for flexible-structure control in Sub- 
section 7.2. 

7.1 The Generic Problem 

Let A, T(t) and B be as in Subsection 3.1, with E an arbitrary real Hil- 
bert space. The differential equation corresponding to (3.2) is, of course, 

z(t) = Az(t) + Bu(t), t > 0. 


We assume that we have a p-dimensibnal measurement vector y(t) given by 
y(t) = C_u(t) + Cz(t), 


where C e L(R°. R^) and C e L(E,rP) for some positive integer p. 
Definition 7.1. For any F e L(rP,E), the system 

5(t) = Az(t) + Bu(t) + F[y(t) - CQu(t)-Gz(t)], t > 0, 


will be called an observer , estimator (we use the terms interchangeably) , for 

A A 

the system (7.1)-(7.2). Let S(t) be the semigroup generated by A-FC. The 

observer in (7.3) is strongly (uniformly exponentially) stable if S(t) is 

strongly (uniformly exponentially) stable. □ 

To justify this definition, we write 


e(t) = z(t) - 'S(t) 


and, with (7.1)-(7.3), obtain 

e(t) = ^(t)e(O), t 2 0. 


Of course, an observer, or estimator. Is necessary because the full state 
2(t) will not be available for direct feedback, and the feedback control must 
be based on an estimate of z(t). When, as in this paper, the desired control 
law has the form 

u(t) = -Fz(t) 


for seme F e L(E,r'"), the observer in (7.3) can be used to construct z(t) from 
the measurement in (7.2) and then the control law in (7.6) can be applied to 
z(t). The control applied to the system is then 

u(t) = -Fz(t). 

and the resulting closed-loop system is 




ll'n - 3.,„(t) J^^>[ . t . 0, 
z(t) z(0)' 


where S^,„(t) is the semigroup generated on E X E by the operator 

fA -BF 1 

^ ra-ni^jftm ' !>(*„.„) = D(A)XD(A). 


With the estimator error e(t) defined by (7.4), it is easy to show that 
(7.8) is equivsilent to (7.5) and 


:(t) = (A-BF)z(t) + BFe(t), t > 0, 


where (A-BF) generates a semigroup S(t) on E. Also, it is easy to prove the 

Theorem 7.2. Suppose that there exist positive cjonstants M^, Kj, a^ and Oj 
such that 

IIS(t)|| i M^ e 



||S(t)|| < M^e , t 1 0. 


Then, for each real Og < minla^, oj) , there exists a constant M3 such that 

II S„.„(t)|| i Mge"^ . t 2 0. ^^^^^^ 


a(A„,J = a(A-BF) U <r(A-FC), ^^ ^^^^ 

where "(A^,^) is the spectrum of A^,^. □ 

The observer in (7.3) and the control Ifw in (7.7) constitute a compensa- 
tor for the control system in (7.1) and (7.2;. 'The transfer function of this 
compensator is 

B(s) =--F(sI-lA-BF + F(C F-C)1)~H' , 

" (7.14; 

which is an m X P matrix function of the complex variable s. When E has 
infinite dimension, the compensator transfer function is irrational, except in 


degenerate, usually unimportant cases. 

The foregoing definitions of this section and Theorem 7.1 are straight- 
forward generalizations to infinite dimensions of observer-controller results 
in finite dimensions. Balas [B3] and Schumacher [S2] have used similar exten- 

Now suppose that F is chosen as 

f = hc*r\ 



Where n e L(E,E) is the minimal nonnegative selfadjoint solution to the Ric- 
cati equation 

AH + HA* - nc* R-^cn + Q = 0. 


A /^ 

With Q e L(E,E) nonnegative and selfadjoint and R c L(rP,rP) symmetric and 
positive definite. Theorem 3.3 (with A, B, Q, R, Hand Sit) replaced by 

♦ •AAA. A* A 

A ,C , Q, R, n, and S (t)) gives sufficient conditions f or fi to exist and for 



the semigroup S (t) ~ and equivalently its adjoint, the S(t) generated by 

A 11 C R c — to be uniformly exponentially stable. 

Definition 1.3 . When the control gain operator is 

F = R -^B*!! , 


with n the solution to the Riccati equation (3.3), and the observer gain 
operator is given by (7.15) and (7.16), we will call the compensator consist- 
ing of the observer in (7.3) and the control law in (7.7) the ont•■^ ,nl;^] infinite 
diffiengl,9n^l compensator, and (7.8) the optimal closed-loop svstem . Q 




z = Az + Bu 
y = CqU + Cz 

Control System 



U = 

[A-BF-F(Cq F-C)] z + F y 


Optimal Infinite Dimensicnal Compensator 

Figure 7 .1 . Optimal Closed-loop Sj stem 

^eaaclsl.i. The infinite dimensional observer defined by (7.3), (7.15) and 
(7.16) is the optimal estimator for the stochastic version of (7.1) and (7.2) 
when (7.1) is disturbed by a stationary gaussian white noise process with zero 
mean and covariance operator Q and the measure«ient in (7.2) is contaminated by 
similar noise with covariance R. For infiiite dimensional stochastic estima- 
tion and control, see [HI. C4] . When the state weighting operator Q in (3.1) 
is trace class, the optimal infinite dimensional compensator minimizes the 
time-average of the expected steady-state yalue of the integrand in (3.1). 
Existing theory for stochastic control of infinite dimensional systems 
requires trace-class Q, but we have a well defined compensator for any bounded 
nonnegative selfadjoint Q and $. as long ss the solutions to the Riccati 


equations exist. As the next two sections show (without assuming trace-class 
Q), the infinite dimensional compensator is the limit of a sequence of finite 
dimensional compensators, each of which can be interpreted as an optimal LQG 
compensator for a finite dimensional model of the structure. Therefore, we do 
not require trace-class Q in our definition of the optimal compensator, even 
though this compensator solves a precise optimization problem only when Q is 
trace class. 

This paper is concerned primarily with how the finite dimensional compen- 
sators converge to the infinite dimensional compensator, and the analysis of 
this convergence requires only the theory of infinite dimensional Riccati 
equations for deterministic optimal control problems and the corresponding 
approximation theory. While the stochastic interpretation of the infinite 
dimensional compensator and, in Section 8.2. of the finite dimensional estima- 
tors should be motivational, nothing in the rest of the paper depends on a 
stochastic formulation. We assume that the operators Q, R, Q and R are deter- 
mined by some design criteria. In many engineering applications, determinis- 
tic criteria such as the stability margin and robustness of the closed-loop 
system, rather than a stochastic performance index and an assumed noise model, 
govern the choice of Q, R. Q and R.Q 

7.2 Application to Structures 

For the rest of the paper, E = V X H as in Section 2, and A and B are the 
operators defined there. 

The measurement operator C in (7.2) now must have the form 


c = [c^ c^] 



where C^ e L(V,rP) and C^ e L(H.rP). Hence, if we denote by (C(x.x))^ the i 
ponent of the p^vector C(x.x). for (x.x) e E. then there must exist c^^ e V 


and c, . e H such that 

{Cix.'x))^ = <Ci^,x>y + <C2i.5>H' i = 1.....P. 


Also, the estimator gain operator F is givnn by 

A P A A 

Fy = I (fi,gi)yi 
i=l 1 ^ -^ 



for y = [y y ... y l*^ e R^, where the fu^iatifinai e9tJ,nat9r £ains f^ and g^ 
are elements of V and H, respectively. 

For the optimal estimator gains, we cm partition 11 as 

ft - 


A« A 

EL, n^ 

and use (7.15) and (7.19) to get 




P A_i A i-i 

z (R ^)ij(no°ij + 'V2j> • 


t, = I (R"^)i.(ftjc + DjCjj) , i = 1.2, ....p. 
^ j=l J J (7.22b) 

Now let us partition Q as in (4.34): 


$ = 


In the optimal control problem, we almost always have a nonzero Q because 
this operator penalizes the generalized displacement. For the results in this 
paper. Q^ can be nonzero in the observer problem, and, as in the control prob- 
lem, some of the strongest convergence results for finite dimensional approxi- 
mations can be proved only for coercive t. However, if the observer is to be 
thought of as an optimal filter, then t should be the covariance operator of 
the noise that disturbs (2.1). in this case, $q = and $ = 0. 


8. Approximation of the Infinite Dimensioral Estimator 

8.1 The Approximating Finite Dimensional Estimators 

Here, the scheme for the approximatior of the flexible structure is that 
in Section 4. We will construct on the sutspace E^ an estimator that apjroxi- 
mates the optimal infinite dimensional estimator of Section 7. and this esti- 
mator will produce an n^^-order estimate ^^ = {^^\) of the infinite dimen- 

• th 1. 

sional state vector z = (x.x). In Section 9. the the n -order compensator 

that results from applying the n^^ approxination to the optimal control law 
(in Section 4.2) to z^ will approximate th( optimal infinite dimensional com- 
pensator of Section 7. 

Hypothesis 8 .1 . There exist a sequence C^ e L(E^.rP) such that 

ilCnPEn-Cll -^0 ^^ "^" (8.1) 

and a sequence Q^ e L(E^) , Q^^ = ^n - °' ^"°^ ^^^ 
Q Pp -> Q strongly as n -» ■' .0 

Hypothesis 8 .2 . For each n. the system (A*,C*) is stabilizable. In particu- 
lar, any unstable modes of the ^stem (C^, \^) are observable. D 

The n^*^ observer , or n estimator , is 

^n = ^n^n ^ V "^ ^n<y-^o"-^n^n> (8.3) 

where the estimator gain F^ is 


^ /v •A.I 

and n^ is the nonnegative selfadjoint solution to the Ricoati operator equa- 

^n^n - fln< " K'^^XK ^ K ^ ^' 


Hypothesis 8.2 implies that such a solution exists and is unique. 


This representation of the n estimator as a system on E , with th 



estimator gain determined by the solution to a Riccati operator equation, is 
necessary for showing how the sequence of finite dimensional estimators 
approximate the infinite dimensional estimator. However, on-line computations 
will be based on the equivalent differential equation 

^ = A°<| + b\ + F"(y-CQU-C"<|) 


where Ti(t) e R , A and B are the matrix representations of the operators A 

and B_, as in Section 4, and c'^ is the matrix representation of C . 
n n 


The 2nxp gain matrix F is 

F" = n"W-» (C")Tr-1. 
where W° is the 2n X 2n grammian matrix in (4.33) and if satisfies 

A'^n" + friw-"(A»)Tw" + n" w-°(c")Tt-V3" + Q" = 0, 

with q"^ the matrix representation of Q^. The relationship between z = 
(Xjj.x ) and ^ is, of course. 




xft) = 2:5i(t)ei 
" i=l (8.9) 


'I - ^^ 5 ^ • (8.10) 

Since the matrix representations of A^ and A^ are a"^ and W-"(A")T w", respec- 
tively, and the matrix representation of C^ is W-'^(C")T. (8.7) is the matrix 

representation of (8.4), and the 2n X2n Riccati matrix equation (8.8) is the 

A ^ 

matrix representation of (8.5), with IT the matrix representation of 11^. 

(Recall that W~" is the inverse of w".) 

As in the control problem, we do not solve the matrix representation of 
the n^*^ Riccati operator equation directly Sjecause the matrix representation 
of a selfadjoint operator in general is not gymmetric. In the duality between 
the optimal control and estimator problems, (8.5) and (8.8) correspond to 
(4.26) and (4.38), respectively. In (4.39). we defined the symmetric matrix 
5" = W"lf and then obtained the Riccati equation (4.40) to solve for DP . 
We proceed in a similar fashion here, but with an interesting difference. 

Since fi and t are nonnegative selfadjoint operators on E^ and IT and 
q" are their matrix representations, the matrices w" fi" and wV are nonnega- 
tive and symmetric. Hence, the matrices 

" - IIW (8.11) 



are nonnegatlve and symmetric. Substituting (8.11) and (8.12) into (8.7) and 
(8.8) yields 

f" = fl"(C")TR-l 



A" fin + fln(An)T _ fin(c")T^Vfl" -. ^" = 0, 


the Riccati matrix equation to be solved numerically in the n^^ approximation 

to the infinite dimensional estimator. In view of the relationship between 

(8.5) and (8.8) and the relationship between (8.8) and (8.14), we see that 

Hypothesis 8.1 guarantees the existence of a unique nonnegative symmetric 

solution to (8.14) . 

To see the relationship between the matrices in (8.14) and the operators 
in (8.5) more clearly — and the difference between the current approximation 
scheme and that used in Section 4.2 for the control problem — suppose that we 
take Q^ = PgjjQlg^. Let q" be defined as in (4.36) and (4.37) with Q^. Q^ . and 
Q2 replaced by Qq, Q ^ , and ^2 • Then 

q" = W-"Q"W-^ 



For example, if Q in the control problem and Z in the estimator problem are 
both equal to the identity, then the q" in (4.35) - (4.42) is w" and 

Q = W . This may seem suspicious, but Subsection 8.2 should demonstrate 
that we are solving the appropriate estimator problem here. 

The only thing missing now for numerical implonentation of the n 



estimator, or observer, is to give C^ the matrix representation of C^. expli- 
citly. We write 

C" = [C; Cjl (8.16) 

where the p x n matrices cj and cj are. respectively, the matrix representa- 
tions of the operators C, and C, in (7.18). We can cover virtually all appli- 
cations by assuming C„= C|,„. in which case the i^^ column of cj is the p- 
vector equal to C,e,. and the i^^ column of cj is the p-vector equal to C,e,. 

Mej^hM^iM ^^ml^ Mi ^ ^a^Uojis far ima^ni^ i,pien, ,nt.tiop of 

iM nth ^^^ ^stlaatai:: For online computation, the n^^ estimator, or 
observer, is (8.6); the gain matrix F" is given by (8.13) and the solution to 

^i /o 1A^ The mahrices q" and C are defined as 
the Riccati matrix equation (8.14). The matrices u 


8.2 Stochastic Interpretation of the Approximating Estimators 

As we have said, our approximation theory for the optimal estimator is 
based on approximation of the infinite dimensional Riccati equation, whose 
structure is the same for both control and estimator problen.s. and the sto- 
chastic properties of the optimal estimator problan never enter our approxima- 
tion theory. Furthermore, using only the deterministic setting above, we will 
proceed, subsequently, to analyze the finite dimensional estimators and the 
compensators based upon them. Nontheless. we should consider momentarily the 
sequence of finite dimensional stochastic estimation problans whose solutions 
are given by the equations of the preceding subsection. 

First, recall how the covariance operator of a Hilbert space-valued rar^ 


dom variable Is defined. The covariance operator of an E-valued random vari- 
able u is the operator Q for which 

expected value {<2,.o>g<'?,„>^} = <Qz.'5>g, z. z e E. 

(See [Bl, C4].) 

With F^ given by (8.4) and (8.5), (8.3) is the Kalman-Bucy filter for the 

^n = Vn "■ ^n" + %' 


^ = S" ^ Vn ^ "-0' 


where <o^(t) is an E„-valued white noise process with covariance operator Q„ 
and u)Q(t) is an R^-valued white noise process with covariance operator 
(matrix) R. Next, careful' Inspection will show that the filter defined by 
(8.6), (8.13) and (8.14) is the matrix representation of the filter defined by 
(8.3), (8.4) and (8.5). 

With z^ and n related as in (4.1) and (4.4), (8.18) and (8.19) are 
equivalent to the system 

Ti = a"ti + b"u + (/, 

y = CqU + c\ + Up. 

'n^*^> by 



where (/(t) is the R^'^-valued noise process related to « (t) 


<o„(t) = I ((/i(t)ei, (/i+n(t)ei). 
" i=l (8.22) 

Certainly, a Kalman-Bucy filter for (8.20) and (8.21) has the form (8.6) with 
the filter gain given by (8.13) and (8.14). This particular filter is the 
matrix representation of the filter defined by (8.3), (8.4) and (8.5) if and 
only if the matrix Q" defined by (8.12) is the covariance of the process (/(t). 
Since q" is the matrix representation of $^, straightforward calculation using 
(8.12) and (8.17) shows that the^" in (8.11) is indeed the correct covariance 

Of course, if a)^(t) and (/(t) represent a physical disturbance to the 
structure, then o.^(t) must have the form (0.a,^2)(t)) and the first n el«nents 
of (/(t) must be zero, but this is not necessary for our analysis. 

The finite dimensional observers can be interpreted now as a sequence of 
filters designed for the sequence of finite dimensional approximations to the 
flexible structure, with the n*^^ approximate system disturbed by the noise 
process a)^(t), whose covariance operator is; Q^. By Hypothesis 8.1, these 
covariance operators converge to the operator ^ of Section 7. If we have a 
reliable model of a stationary, zer(^mean fiiaussian disturbance for the struc- 
ture, then we can take the covariance operator for this disturbance to be ^ 
and think of the infinite dimensional observer as the optimal estimator. But, 
again, this interpretation is not necessarj for the rest of our analysis. 

8.3 Ihe Approximating Functional Estimator Gains 

The n^*^ estimator gain operator in (8.4) has the same form as the infin- 
ite dimensional estimator gain in (7.15) and (7.20). We have 



r^ ^ 




for y - ly^ 72 ... Yp ] e R^, where the functional estimator gains f and 
%n ^^^ elements of V^ = H^. the matrix f" In (8.7) and (8.13) is the matrix 


representation of F^, which means that, if we write 

^1 /2 ... 


^1 „®2 ... 


f g 

where the columns p ^, p "^ e r", th 




n fi 

j=l -^ -^ 

1 = 1, . . . ,p. 


n g^ 

in ~ ^ Pi e^ i = 1, . . .,p. 
j=l ■^ J' 


For convergence analysis, it is useful to note that f^ and g. are given 
also by equations corresponding to (7.22). With the measurement operator C 
written as in (7.19) and C^ = c|g , we have 


m = J^(« ^Ij^nOn^Vn^lj ^ DlnW2j> 






^n%n°2 j^ ' 



\ = 









8.4 Convergence 

Now we will indicate the sense in whicii the finite dimensional 
estimators/observers approximate the infinjte dimensional estimator in Section 
7. As we have said, implementation of the n^"^ estimator is based on (8.6), 
(8.13) and (8.14), but convergence analysi:; is based on the equivalent system 
(8.3), (8.4) and (8.5). The question then is how the observer in (8.3). with 
gain given by (8.4) and (8.5), converges to the observer in (7.3) with gain 
given by (7.15) and (7.16). 

Recall Hypothesis 8.1, and recall frm Section 4 that the approximations 
to both the open-loop semigroup and its adjoint converge strongly. Also, 
recall that Hypothesis 8.2 guarantees a unique nonnegative selfadjoint solu- 
tion to the Riccati equation (8.5) for eadi n. Replacing A^ and B^ with A^ 
and C* in Theorems 5.1 and 5.3, we obtain 

Theorem 8 .3 . i) If || EL 1 1 is bounded uniformly in n, then the Riccati 

A A 

algebraic equation (7.16) has a nonnegative selfadjoint solution H and !!„?£„ 
converges weakly to fl. ii) If there exist positive constants M and p, 
independent of n, such that 

llexpCU^-V^'X'"" ^ *'^' • '^°- ,8.28) 

A ^ 

then II fi^ II is bounded uniformly in n, D^ Pg„ converges strongly to H and 
exp([A -nnC*R"^C^]t)PEn converges strongly to t(t), the semigroup generated 
by A-nc*R"^C, the convergence uniform in t 10. iii) If Q^ is bounded away 
from zero uniformly in n, then ||n;j|| being bounded uniformly in n guarantees 
the existence of positive constants M and P for which (8.28) holds for all n. D 

The proof of the following theorem is practically identical to that of 
Theorem 5 .4. 

IhSSrm 1.4. if Q is E-coercive and d^ = o, then there is no nonnegative sel- 
fadjoint solution of the Riccati operator equation (7.16), and 


II Djjll -> » as n -)^ ». □ 


Our purpose for bothering to state this obvious dual result is to point out 
the following question. Can Theorem 8.4 be modified to include the case where 
Q has the form (7.23) with Qq = 0, Q^ = and Q^ coercive on H? 

Next, we have the dual result to Theorem 5.6: 

Th?«?r?ff l 8.5 Suppose that A^ has an invariant subspace Vq which is also invari- 
ant under the damping map A^^A^, that E^ = V^ X Vq is an observable subspace, 
and that the restrictions of A^ and d^i' ,') to V^ are both H-coercive. Also, 
suppose that V^ has finite dimension n^ and that, for each n 2 n^ in the 
approximation scheme, the first n^ e^'s span Vq and the rest are orthogonal to 
Vq in both V and H. 


i) Then (7.16) has nonnegative solution 11, and II IL|| is bounded uniformly 
in n, so that 3 Pg converges to U weakly. 

ii) If Eq and Eq (the E-orthogonal complement of Eq) are invariant under Q, 
and if the E^-part of the gystem (A,Q) is controllable, then the hypothesis of 
Theorem 8.3 ii) holds. 

£Cfflfif. The proof is practically identical to that of Theoran 5.6 with B 
replaced by C . For ii) , note that, when we partition A and ^ as in (3.16), 


the finite dimensional system (A^^.'q^^) is controllable if and only if the 
system ^Qj^i'^ii) ^s observable. D 

Remark 8.1. Remarks 5.7 and 5.8 pertain to Theorem 8.5 as well as to Theorem 
5.6; i.e., in most applications the theorem requires either that both A^ and 
dp be coercive (so that Vq = {0}) or that the natural mode shapes be the basis 
vectors and the damping not couple the natui-al modes. It seems unlikely that 
a finite number of observable rigid-body modes could change the nature of the 
convergence, but they greatly complicate the proofs. For applications where 
both rigid-body displacement and rigid-body velocity are measured, a result 
analogous to Theorem 5 .9 can be obtained, but we will not bother here because 
it adds no significant insight and we cannot use it in the example in Sections 
6 and 10. Also, see Remark 10.1. D 

Theorem 8.7. If fi^Pg^ converges strongly toll, then 

ll^in-'^iilv-^^ (8.30a) 

llti„-ajlH-^0' " "^-^"' (8.30b) 

where f . and g^ are the functional estimator gains in (7.20) and f^^ and g^^^ 
are the approximating functional gains in (8.25). 

Proof . The result follows from (7.22) and (8.26). D 


9. The Finite Dimensional Compensators and Realizable Closed-loop Systems 
9.1 Closing the Loop 

The n compensator consists of the n'^^ approximation to the optimal con- 
trol law in Section 4. applied to the output of the n*^^ estimator/observer in 
Section 8; i.e., the feedback control 

u = - F z 
n '^n^n 



'n - H"'<n„ 

(recall (4.25)) and $^(t) is the solution to (8.3). Equivalently, this com- 
pensator can be written as 

u„ = -F"^ 



f" = R-^B^ff^ 


(recall (4.43)) and the 2n-vector riit) is the solution to (8.6)). On-line com- 
putations will be based upon the latter representation, and the block diagram 
in Figure 9.1 shows the realizable closed-loop system that results frco the 
n compensator. We will refer to this system as the i^^ SilOSM-lSiSS system . 


z « Az + Bu 
y = Cqu + cz 

— > 

Control System 

A „ [An-enpn + F°(CoF'^cn)]Ti + W 


%- - 


nth Compensator 
Figure 9.1 n^^ Closed-loop System 

This closed-loop system is equivalent to 





where the operator 


r A — BF 1 


generates the closed-loop semigroup S^,^(t) on EXE^. The closed-loop 
response produced by the n^b compensator — i.e.. the response of the n^^ 
closed-loop system — can be written then as 






Note that A^^ ^ ^as oompaot resolvent if and only if A does. 
9.2 Convergenoe of the Closed-loop Systems 

Now we will consider the sense in whidi the n*^'^ closed-loop system 
approximates the optimal closed-loop system in Section 7 (Definition 7.3). 
Recall from Sections 4.1 and 8.1 how the approximating open-loop semigroups 
Tjj(*) and their adjoints converge strongly and how the input operators B^, the 
measurement operators C^ and their respective adjoints converge in norm. Sec- 
tions 5 and 8 have given sufficient conditions for the approximating control 
and estimator gains to converge to the gains for the optimal infinite dimen- 
sional compensator. In this section, we will assume 

Hypotheses 9.1 . As n — > «, 


llF„-F||-^0. D 


Remark 9 .2 . Of oourse, we are interested primarily in the case where the 


gains F and F are the optimal LQG gains in (7.15) and (7.17) and F ^nd F are 
the corresponding approximations in Sections 4 and 8 (i.e. ,(9.2) and (8.4)). 
However, fca* the analysis of this section, we need only Hypothesis 9.1 for 
some F e L(E,Rn), F e L(rP,E) and approximating sequences F^ and F^. Any such 
gain operators will yield closed-loop semigroup generators A^,^ in (7.9) and 
A„,Q in (9.6). a 


We denote the projection of E K E onto E X E^ by ?^^^. 

Theorem 9.3. For t 10, S^.^^^^^EEn ^^^^^^'^ strongly to S„,„(t), and the 
convergence is uniform in t for t in bounded intervals. 

Proof . This follows from the strong convergence of the open-loop semigroups 
and the uniform norm convergence of the control and estimator gains. D 

We should expect at least Theorem 9.3, but we need more. We should 
require, for example, that if S(t) is uniformly exponentially stable, then 
S . (t) must be also for n sufficiently large. Although nunerical results for 
nunerous examples with various kinds of damping and approximations suggest 
that this is usually true, we have been unable to prove it in general. We do 
have the result for the following important case. 

Suppose that the basis vectors e of the approximation scheme are the 
natural modes of undamped free vibration and that the structural damping does 
not couple the modes. Then, for each each n, E^ and E^^ reduce the open-loop 
semigroup T(t) and its generator A. For this case, we can extend A^.^ to D(A)X 
D(A) as 

A = |a 

»'n \fj 

L n 


c T 1 

nCompJ (9.10) 


"^nComp = lAn-Vn-Vn^^En ^ ^•D(A)n E^* 


Note that E is the span of the modes not represented in the n compensator. 
The operator A , generates a semigroup "s^.^tt) on E X E, E X E^^ and 



{0}XE^ reduce "s^. ^(t) , and the restriction of "s^.-Ct) to E X E is S . (t). 
Hence S^,^(t) is uniformly exponentially stable if and only if both S„, (t) 
and the part of the open-loop system on E^ are uniformly exponentially stable. 

Theprem 9.4. i) Suppose that the basis vectors of the approximation scheme 
are the natural modes of undamped free vibration and that the structural damp- 
ing does not couple the modes. Then S (t) converges in norm to S . (t), 

*** n 00 CD 

uniformly in bounded t-intervals. 

ii) If, additionally, S^,^(t) is uniformly exponentially stable, then S , (t) 


is uniformly exponentially stable for n sufficiently large. 
Proof . From (9.6), (9.10) and (9.11), we have 

B[F„P„ -F]■ 

A , -A , 

00 » 00 "oo ' [] 

A. /V 


n J 



^n= ^Vn^En-SP) ^ ^Vn^En-^^). 


Therefore, I |A„,^-A„,^|| — > as n — >». and the theoron follows. D 

This paper empiiasizes using the convergence of the approximating control 
and estimator gain operators F^ and F^^, and the convergence of the functional 
gains that can be used to represent these operators, to determine the finite 
dimensional compensator that will ja-oduce essentially optimal closed-loop per- 
formance. However, close examination of the right sides of (9.12) and (9.13) 
reveals another important convergence question. While the gain convergence in 
(9.8) and (9.9) drives the off-diagonal blocks in (9.12) to zero, the norm 
convergence of the approximating input and output operators also is essential 

94 C/-- 

in killing A . Expanding the two terms in this block yields 

B F P„ - BF = B„(F„Pp. -F) + (B -B)F, 
n n En n n En n (9.14) 

F C P„ - FC = (F-F)C„Pp„ + F(C^Pp.-C). 
n n En n n En n t,n (9.15) 

The second term on the right side of each of these equations represents, 
respectively, control and observation spillover, which has been studied exten- 
sively by Balas. Together, the control spillover and observation spillover 
couple the modes modelled in the compensator with the modes not modelled in 
the compensator. The spillover must go to zero — as it does when B^ and C^^ 
converge — for A^ „ - A^ to go to zero. 

We should ask then whether there exists a correlation between the con- 
vergence of F and F and the elimination of spillover. The answer is yes if 
n n 

no modes lie in the null space of the state wei^ting operator Q in the per- 
formance index and if the assumed process noise, whose covariance operator is 
Q, excites all modes, but this correlation is difficult to quantify. As we 
discussed in Section 6.4, the two main factors that determine the convergence 
rates of the gains are the Q-to-R ratio and the damping, neither of which 

affects the convergence of B and C„. On the other hand, when either factor 

n n 

(small Q/R or large damping) causes the gains to converge fast, it generally 
also causes the magnitude of F and F to be relatively small, thereby reducing 
the magnitude of the spillover terms in (9.14) and (9.15). Also, as n 
increases, the increasing frequencies of the truncated modes usually reduce 
the coupling effect of spillover. This is well known, although it cannot be 
seen from the equations here. In examples that we have worked, we have found 
that when n is large enough to produce convergence of the control and 
estimator gains, the effect of any remaining spillover is negligible. But 
this may not always be true, and spillover should be remembered. 


9.3 Convergence of the Compensator Transfer Functions 

The transfer function of the n^^ compensator (shown in the bottom block 
of Figure 9.1) is 

a„(s) =-F„(sI-U„-B„F„+t„(CoF„-C„)])-^V ^^.^^^ 

which is an m X p matrix function of the complex variable s for each n, as is 
the similar transfer fvinction ffi(s) in (7.14) for the infinite dimensional com- 
pensator. We continue to assume Hypothesis 9.1. 

We will denote the resolvent set of [A-BF+F(CpF-C)] by 

Theorem £.5. There exists a real number a^ such that, if Re(s) > a^, then s e 
p([A^-Bj^ Fj^+Fjj(CqFjj-Cjj)] ) for all n, and ^jyis) converges to 5(s), imiformly in 
compact subsets of such s. 

Proof . The operator [A-BF+F(CqF-C)] is obtained from a contraction semigroup 
generator by perturbation with bounded operators, and the approximations to 
the perturbing operators sire bounded in n, by strong convergence. In view of 
this, close examination of the basic approximation scheme in Section 4.1 will 
show that there exists a bound of the form lA^exp(aj^t) , independent of n, for 
the semigroups generated by lA^-Bj^ ^n'*'^n^^0^n~'^n^^ • ^^^o, these semigroups 
converge strongly to the semigroup generated by [A-BF+F(CqF-C)] , according to 
[G3, Theorem 6.6]. For Re(s) > a^ then, the resolvent operator in ^^(s) con- 
verges strongly to that in $(s), uniformly in compact s-subsets, by [Kl, page 
504, Theoran 2.16, and page 427, Theorem 1.2]. □ 


This result leaves much to be desired. For example, it does not guaran- 
tee that any subset of the imaginary axis will lie in 

p([A -B F +F (CnF - C„)]) for sufficiently large n, even if all of the ima- 
nnnnun n 


ginary axis lies in p([A-BF+F(CqF-C)] ) . As with the convergence of the 
closed-loop systems, we can get more for certain important cases. 

Remark 9.6. If the open-loop semigroup T(«) (whose generator is A) is an ana- 
lytic semigroup, then there exist real numbers a, 6 and M, with 9 and M posi- 
tive, such that p([A-BF+F(CqF-C)]) contains the sector {s : larg(s-a) |< f + ^^ ' 
and for each s in this sector, 

||(sI-[A-BF+F(CqF-C)])"^II < M/|s-a| 

Theorem 9.7. i) If the basis vectors of the approximation scheme are the 

natural modes of undamped free vibration and the structural damping does not 

couple the modes, then each s in p( [A-BF+F(CqF-C)] ) is in 

p([Ajj-B Fn"^Fn^^0^n"'^n^^^ ^""^ "^ sufficiently large and ff„(s) converges to !B(s) 

as n — > « , uniformly in compact subsets of p( [A-BF+F(CqF-C)] ) . ii) If, 

additionally, T(») is an analytic semigroup, then S^(s) converges to »(s) uni- 
formly in the sector described in Remark 9.6. 

Proof , i) In this case, we have also 

*n(s) = VEn^s^ -''^nComp)'^' 


where If _ is the operator on D(A) defined by (9.11). The result follows 

from (9.8) and (9.9) and the fact ^^at li^^ converges in norm to 

[A-BF+F(CqF-C)] . ii) The result follows from i) and a bound on 

(sI-A „ )~^ for large |s| that is obtained from the Neumann series in view 
nComp' ' ' 


of (9.17) and the uniform-norm convergence of A „ 

° nComp* 

Theorem 9.S. If A has compact resolvent, then S (s) converges to !5(s) for 
each s e p( [A-BF+F(CqF-C)] ) , uniformly in compact subsets. 

Proof . As a result of Theorem 4.4, the resolvent operator in S (s) converges 
in norm to the resolvent operator in ffi(s) for sufficiently large real s. 
After an artificial extension of A^ to E^ then, the present theorem follows 
from [Kl, pages 206-207, Theorem 2.25]. 


10. Closing the Loop in the Example 

As in Definition 7.3, the optimal closed-Loop system is formed with the 
optimal infinite dimensional compensator, whicn consists of the optimal con- 
trol law for the distributed model of the structure applied to the output of 
an optimal infinite dimensional state estimator. This optimal control law is 
the limit of the approximating finite dimensional control laws in Section 6. 
in this section, we first approximate the infinite dimensional estimator, as 
in Section 8. and then apply the approximating control laws in Section 6 to 
the approximating finite dimensional estimators to produce a sequence of fin- 
ite dimensional compensators that approximate the optimal compensator. 

10.1 The Estimator Problem 

We assume that the only measurement is tlie rigid-body angle 9 and that 

A _ 4 

this measurement has zerc^-mean Gaussian white noise with variance R - 10 . 

We model the process noise as a zero-mean Gaussian white disturbance that has 

a -K^mponent distributed uniformly over the beam, as well as two concentrated 

components that exert a force on the tip mass and a moment on the hub^ For 

this disturbance, the covariance operator Q has the form (7.23) with 0^= 0, 

A A 

Qj^ = and Q2 = I- 

We construct the approximating estimators as in Section 8.1. The gain 
for the nth estimator is given by (8.13) with the solution to the Riccati 
matrix equation (8.14). For the rigid-body measurement, the matrix C" is 

cn=[lOOO.,.]. ^^^^^^ 


According to (8.15), the matrix Q" it 
\n P* 0* ] 


since W'^ is the matrix in (4.33). (As always, M"" is the inverse of the mass 

matrix.) Recall from Section 6.3 that n = 2n + i „h«,.^ . .u 

xHg + 1 where n^ is the number of 


Our only use for the functional estimator gains is to measure the conver- 
gence of the finite dimensional estimators to the optimal infinite dimensional 
estimator. To see the convergence of the approximating estimator gains, we 
compute the approximating functional estimator gains as in Section 8.3. Like 
the functional control gains, the functional estimator gains have the form 

^ = («f.«> . 


« = <°g'«^g.Pg) ' 

and the corresponding approximations have the form 


^n ~ (o*»^»0 .B ) . 
" in"^gn"^gn' ' 



^n - <°gn'«*gn'Pgn> • 


Mmsk 10.1 We cannot guarantee as much about convergence for the approximat- 
ing estimators as we could for the approximating control problems in Section 
6. Since the damping in this example does not couple the natural modes and 
the rigid-body mode is observable, we would have Part i) of Theorem 8.5 if we 
were using the natural mode shapes as basis vectors. Therefore, we know at 


least that a solution to the infinite dimensional Riccati equation (7.16) 
exists and that the infinite dimensional estimator that we want to approximate 
exists. The numerical results indicate that the solutions to the finite 
dimensional Riccati equations are bounded in n and that the functional esti- 
mator gains converge in norm. The rigid-body mode prevents our guaranteeing a 
priori all the convergence that we want. If a torsional spring and damper 
were attached to the hub in the current example, we would have coercive stiff- 
ness and damping and Theorem 8.5 11) would guarantee that the solutions to the 
finite dimensional Riccati equations converge strongly and that the functional 
estimator gains converge in norm for the basis vectors used here. Also, see 
Remark 6.2 and Remark 8.6. D 

For damping coefficient c^ = lo""*. Figures 10.1 and 10.2 show ^^^ and <^^. and 
Tables 10.1 and 10.2 list the the scalars a^n-Og^ and Pg^. Since ^f^^O) = 
-*' (0) =0, the convergence of (,'' implies the convergence of p^.^ = iftf^C^); 
as in the control problem, p^.^^ is not an Independent piece of information 
about the estimator gains while, as far as our results go. Pg„ is. We main- 
tain analogy with the control problem and list only pg^ in the table. 





^'- '*^ ^ -~^^ ~ r r 3 1 

lb. 00 pb. 00 3b. 00 vb.oo sb.oo sb.oo 7^ 

00 80.00 


00 I bo. 00 

Figure lO.lft. Functional Estimator GEiin Component ^_ 

-4 -4 

Damping Cj, = lO ; estimator R = 10 ^ 

Figure 10 'li- Functional Estimator Gain Component 


Damping c^ = lo ; estimator R 


number of elements n = 2,3.4,5,8 

number of element n = 2, 3, 4, 5, 8 



' 3i » t 

-B-. -^^1 

,TSi J^o ^^0 ^:^o i^TS^ ^^' ^^^ ^^' ^^' ^-^ 

Figure 10.2a. Functional Estimator Gain Component ^^^ 
Damping Cq = 10 . estimator R = 10 

number of elements n^ = 4, 6, 8, 10 

1 ~ 








Tl 50 





o z 

o s 




=i - 


-< w 



- lO 

C3 ■ 













p 0.00 lb. 00 j'o.oo 30.00 lib. 00 sb.oo s'o.oo 70.00 «o7oo 

Figure 10.2Jj, Functional Estimator Gain Component 
Damping C„ = lo"^; estimator R = 10"'* 

number of elements n^ = 4, 6. 8, 10 

"e °fn «gn Pgn 

2 5.0358 12.680 -1334.9 

3 5.2514 13.789 -1455.4 

4 5.3195 14.149 -1495.7 

5 5.3478 14.300 -1512.2 
8 5.3611 14.371 -1520.1 

Tafeifi 10 .1. Scalar Components of Functional Estimator Gains 

Damping coefficient Cq = lo"'* ; estimator R = 10"'* 

number of elements n = 2,3,4,5,8 

e fn "gn Pgn 

4 5.3195 14.149 -1495.7 

6 5.3567 14.347 -1517.5 

8 5.3611 14.371 -1520.1 

10 5.3623 14.377 -1520.8 

labifi 10.2. Scalar Components of Functional Estimator Gains 

Damping coefficient c^ = lo"'*; estimator R = 10""* 

number of elements n. = 4, 6, 8, 10 


Figures 10.3a and 10.3b and Table 10.3 give the numerical results for the fin- 
ite dimensional estimators when the structural damping is zero. While Theorem 
8.4 says that the solutions to the finite dimensional Riccati equations for 
these estimators will not converge when the damping is zero and Q is coercive 


on E. we have no result to predict the convergence for zero damping when Q is 
not coercive (even though Qj is coercive on H) . From the numerical results 

though, f does not appear to converge. 
^^ n 

"e °fn °gn ^gn 

2 5.0730 12.868 -1354.4 

3 5.3390 14.253 -1506.0 

4 5.4417 14.806 -1568.0 

5 5.4894 15.067 -15 96.3 
8 5.53 98 15.345 -1627.2 

Table 10.3 . Scalar Ctoraponents of Functional Estimator Gains 

Zero damping; estimator R = 10 

nvmiber n = 2 , 3 , 4 , 5 , 8 
























ih.oa ;b.oo jb. oo vb.oo sKj 

6b, 00 7b. 

1.00 9b. 00 ibo.oo 

n&urea 10.3a. Functional Estimator Gain Component «(" 
Zero damping; estimator R = 10~^ 

number of elements n^ = 2. 3, 4. 5. 8 

'°-''° '^'' '^^ ^^' ^^0 ^^0 i^o 1^, iST^o ,l,:S n.0.00 

fiance lO-lb. Functional Estimator Gain Component ^ 

Zero damping; estimator R = 10 ^ 


number of elements n 

2, 3, 4, 5, 8 

10.2 Approximation of the Optimal Compensator 

Finally, for the damping c^ = lO"^ R = -05 in the control problem and R 
= 10"* in the estimator problem, we construct the finite dimensional compensa- 
tor in Figure 9.1; i.e. for each n = 2n^ + 1. we apply the nf control law 
represented by the functional gains in Figure 6.4 and Table 6.4 to the output 

of the n^^ estimator represented by the functional gains in Figure 10.2 and 

Table 10.2. As the number of elements increases, the transfer function in 

(9.16) of the finite dimensional compensator converges to the transfer func- 
tion in (7.14) of the optimal infinite dimensional compensator, as described 
in Section 9.3. Theorem 9.5 and Remark 9.6 apply. Figure 10.4 shows the fre- 
quency response (bode plots) of the finite dimensional compensators f or 4 . 6 . 
8 and 10 elements. The phase plot is for 10 elements only. These plots indi- 
cate that the finite dimensional compensator for eight or more elements is 
virtually identical to the optimal infinite dimensional compensator, as 
predicted by the functional gain convergence in Figures 6.4 and 10.2. 




0-' ^ j^ 56789^0- i 3ite7^h\0' ^ ^^ih H^\o' ^ hi^i^H^\^ ^ U^^i 


\o-' ^ i^ihHi\o-' ^ iTJIH^^o''^ W^ y^Ho- ^ UUU^^o' ^ UU^A^ 


Uemm 10.4. Frequency Response (Bode Plot) of Compensator! 
Damping o^ = iq-^; control R = .05, estimator R = lO"'* 
number of elements n = 4, g, g^ jq 


10.3 Conunents on the Structure and Dimensior of the 
Implementable Compensators 

Though this paper does not address the problem of obtaining the lowest- 
order compensator that closely approximates the infinite dimensional compensa- 
tor, we should note that the compensators based on eight and ten el«nents here 
are unnecessarily large because the finite element scheme that we chose is not 
nearly the most efficient in terms of the dimension required for convergence. 
(The dimension of the first-order differential equation in the compensator is 
2(2ne+l).) We used cubic Hermite splines here to demonstrate that the finite 
element scheme most often used to approximate beams in other engineering 
applications can be used in approximating the optimal compensator. In [G5l . 
we compare the present scheme with one using cubic B-splines and one using the 
natural mode shapes as basis vectors. The natural mode shapes yield the 
fastest converging compensators, but the B-splines are almost as good. The 
only advantages of the Hermite splines result from the fact that the coding to 
build the basic matrices (mass, stiffness, etc.) is simpler than for B-splines 
and the fact that, before the Riccati equations based on, say, ten natural 
modes are solved, a much larger finite el«nent approximation of the structure 
must be used to get the ten modes accurately. 

To understand the redundancy in the large finite dimensional compensators 
here, it helps to consider the structure of the optimal compensator. It is 
based on an infinite dimensional state estimator that has a representation of 
each of the structure's modes. In the present example, the optimal compensa- 
tor estimates and controls the the first six modes significantly, the next 
three modes slightly, and virtually ignores the rest. This observation is 


based on the projections of the functional gains onto the natural modes and on 
comparison of the open-loop and closed-loop eigenvalues. (See [G5] for more 
detail, including the spectrum of closed-loop system ~ which is stable — 
obtained with the ten-element compensator here.) The infinite dimensional 
compensator then has an infinite number of modes that contribute nothing to 
the input-output map of the compensator. These inactive modes are just copies 
of all the open-loop modes past the first nine. They can be truncated from 
the compensator without affecting the closed-loop system response signifi- 
cantly. The number of active modes in the compensator ~ i.e., the modes that 
contribute to the input-output map — depends on the structural damping and 
the Q's and R's in the LQG problem statement. (See the discussion in Section 
6.4 about the effect of damping and control weighting on performance.) 

The compensator computed here based on ten elements has 21 modes 
(although we did not do the computations in modal coordinates). Nine of these 
compensator modes are virtually identical to the nine active modes in the 
infinite dimensional compensator, and the twelve inactive modes are approxima- 
tions to the tenth through twenty-first open-loop modes of the structure, "me 
inactive modes result from the large number of elements needed to approximate 
the active compensator modes accurately. Now that we essentially have the 
optimal compensator in the ten- element compensator, we could truncate the 
twelve inactive modes and implement a compensator with nine modes. And we 
probably could reduce the compensator even further using an order reduction 
method like balanced realizations. 


11. Conclusions 

For the deterministic linear-quadratic optimal regulator problem for a 
flexible structure with bounded input operator (the B^ in (2.1)), the approxi- 
mation theory in Sections 4 and 5 is reasonably complete. Ilie most important 
extensions should be to the corresponding (vtiry difficult) problem with 
unbounded input operator, for which there exists little approximation theory. 
Because of the different kinds of boundary input operators, stiffness opera- 
tors and structural damping, all of which must be considered in detail when B^ 
is unbounded, it seems unlikely that the approximation theory for the 
unbounded-input case can be made as complete as the theory here. 

The convergence results in Section 8 for the estimation problem are less 
complete than those for the control problem because rigid-body modes present 
more technical difficulties for the proofs in the estimator case. However, 
our analysis and numerical experience suggest that the difficulties only make 
the proofs harder and that the convergence in the estimation problem is ident- 
ical to the convergence in the control problem, and that controllable and 
observable rigid-body modes make no qualitative difference in either problem. 

Where we would most like substantial improvement over the results of this 
paper is in Section 9.2. which considers how the approximating closed-loop 
systems obtained by controlling the distributed model of the structure with 
the finite dimensional compensators converge to the optimal closed-loop sys- 
tem, obtained with the infinite dimensional compensator. Theoreoi 9.4 gives us 
what we want for problems where the damping does not couple the natural modes 
of free vibration and the natural mode shapes are the basis vectors for the 


approximation scheme. In particular, this theorem says that, if the optimal 
closed-loop system is uniformly exponentially stable, then so are the approxi- 
mating closed-loop systems for sufficiently large order of approximation. We 
have verified numerically the stability of the approximating close-loop sys- 
tems for the example in Sections 6 and 10, where the basis vectors are not the 
modes. This example and others have made us suspect that Theorem 9.4 is true 
when the basis vectors satisfy Hypothesis 4.1 only and when the damping cou- 
ples the modes. 

Another possible approach to analyzing the convergence of the approximat- 
ing closed-loop systems to the optimal closed-loop system is to use the 
input-output description in frequency domain. Results like those in Section 
9.3 are useful for this, although for the closed-loop stability we want, we 
probably need the transfer functions of the finite dimensional compensators to 
converge more uniformly on the compensator resolvent set than we have proved 
here. In our example. Figure 10.4 indicates that these transfer functions 
converge uniformly on the imaginary axis, but we have no theorem that guarar^ 
tees this. 



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Contr. Opt. . 18(1980) pp. 311-316. 

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Control Problems on Hilbert Spaces," SIAM J. Contr. 
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Schemes for Optimal Control of Flexible Structures," to 
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Errata for [Gl] 
In the first paragraph of the proof of Theorem 2.1 on page 
689 of [Gl], the first sentence should be: 

If a dissipative operator is invertible, its inverse is 
At the beginning of the fifth line of the same paragraph, the 
expression (ax+y) should be deleted the first time it occurs. 
The next-to-last sentence of the paragraph should be: 

Hence, if a densely defined maximal dissipative operator has 
dense range, its inverse is maximal dissipative. 
The theorem is correct as stated. 

In the current paper, we use Theorem 2.1 of [Gl] to conclude 
that the operator A defined in Section 2 is maximal dissipative (see 


(2.10)-(2.12)) and that the operator A in (2.16) has a unique maximal 
dissipative extension. 




Report Documentation Page 

1 Report No 

NASA CR- 181705 
ICASE Report No, 


2. Government Accession No. 

4 Title and Subtitle 


3. Recipient's Catalog No 

5 Report Date 
August 1988 

7 Author(s) 

J. S. Gibson and A. Adamlan 

9 Performing Organization Name and Address 

Institute for Computer Applications In Science 

and Engineering 
Mail Stop 132C, NASA Langley Research Center 
Hampton. VA 23fif.'S-S?? ' ) 

6 Performing Organization Code 

8 Performing Organization Hepon No 

12 Sponsoring Agency Name and Address 

National Aeronautics and Space Administration 
Langley Research Center 
Hampton, VA 23665-5225 

15 Supplflrnentary Notes 

Langley Technical Monitor: 
Richard W. Barnwell 

Final Report 

10. Work Unit No. 


11. Contract or Grant No 

NAS1-17070, NASl-18107 

13. Type of Report and Period Covered 
Contractor Report 

14 Sponsoring Agency Code 

Submitted to SIAM Journal of 
Control and Optimization 

16, Abstract j^ls paper presents approximation theory for the llnear-auarfr;,t fr r= < 

finite element or mr,/.! ^^^^"f \°'^ ^ sequence of finite dimensional, usually 
T„. p^ ^7"^""^ °/ "°''^1' approxitnatlons of the distributed model of the structure 

The n^^rdIl:n:rnal°"' "'T'l ^'^ "'""°" ^° "^^ approxlmatrng rob : 
eluding formulas f «q"atlons for numerical approximation are developed, in- 

uo^^iMpTrst^tt; rri;t"^eorprri:o:°:f^-;ainrb;^^7n°difT/r"%'° r- h- 
L^:r::;oL°;;/7i7i7:"d^i " t ?-°— - --- "- ::.ttii:i7sZ':z : z 

Lorresponaing finite dimensional compensator"; 1c ci-„^< = j »i 

17 Key Words (Suggested by Author(s)) 

control, flexible structures, optimal 
control, approximation 

19 Security Classif (of this reponi 


18. Dittrtbution Stalenwnt 

08 - Aircraft Stability and Control 
63 - Cybernetics 

Unclassified - unlimited 

20. Security Classif (o1 this page) 

21 No of pages 


22 Price 

NASA-Langley, 1988