Abstract:
An optimal control system for canceling the undesired responses of a linearistributed-parameter system in response to an input stimulus. The control system is optimally distributed and its design is based on the extension of the Pontryagin&#39;s maximum principle for a lumped-parameter system to a distributed-parameter system. The technique is to transform the stimulus-to-undesired-output transfer function of the distributed-parameter system into a multidimensional discrete state-space model, and to require a performance criterion to be defined. Theorems have been established to give conditions for asymptotic stability of the closed-loop distributed-parameter system, and to set up weighting factors for the performance criterion. An optimal distributed-control system can force the states of the distributed system to behave according to the prescribed performance criterion regardless of the boundary and initial conditions. The optimal control law can be implemented either as a state-feedback or an output-feedback controller.

Description:
BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     The invention relates to control systems directed by digital computers, and more particularly to a control system for use in connection with a physical system. The physical system responds to an input stimulus by generating an undesired output which affects a physical body, in addition to its desired outputs. The distributed control system generates a state-space model of the transfer function between stimulus and undesired output and, using optimizing procedures which use a set of optimal control gains, generates a derived output from that space-state model which approximates the undesired output for a given stimulus. The control system then supplies the derived output to a transducer which applies a transducer output to the physical body to cancel the undesired output. 
     2. Description of the Prior Art 
     A number of modeling procedures have been developed in the prior art to control the dynamics of a continuous distributed-parameter system in convenient forms. These include a first-order system of partial differential or difference equations method proposed by Wang and Tung in 1964, in a paper entitled &#34;Optimum Control of Distributed-parameter Systems&#34;, in Journal of Basic Engineering, Trans. ASME, pages 67-78; a separation method proposed by Meirovitch in 1967 in a book entitled &#34;Analytical Methods in Vibrations (New York: The Macmillan Co.), and by Meirovitch and Silverberg in 1983 in a paper entitled &#34;Globally Optimal Control of Self-adjoint Distributed Systems&#34;, in Optimal Control Applications and Methods, Vol. 4, pages 365-386; and a multidimensional state-space method proposed in various forms by Attasi in 1973 in a paper entitled &#34;Systems Linearies Homogenes a Deux Indices&#34;, in IRIA Rapport Laboria, No. 31; by Roesser in 1975 in a paper entitled &#34;A Discrete Space-State Model for Linear Image Processing&#34;, in IEEE Transactions on Automatic Control, Vol. AC-20, No. 1, pages 1-10; by Fornasini and Marchesini in 1978 in a paper entitled &#34;Doubly-Indexed Dynamical Systems: State-space Models and Structural Properties&#34;, in Math. Systems Theory, pages 59-72; and by Kaczorek in 1985, in a book entitled Two-dimensional Linear Systems (Heidelberg, Germany: Springer-Verlag). I have also done work in this area in my doctoral thesis, made publicly available less than a year prior to the filing date of this application, entitled Multidimensional Discrete State-space Modeling Optimal Control and Tracking of The Linear Distributed-parameter Systems, D.Sc. Dissertation, School of Engineering and Applied Science, George Washington University, Washington, D.C. 
     This application discloses a control system for controlling the dynamics of a distributed-parameter system in an optimum manner. Many researchers have developed techniques to manipulate the dynamic characteristics of the distributed-parameter systems. These include Wang and Tung in the work cited above; Paraskevopoulos in 1979, in an article entitled Eigenvalues Assignment of Linear Multivariable Two-dimensional Systems, in Proceedings of the IEEE, Vol. 126, pages 1204-1208; Paraskevopoulos and Kosmidou in 1981, in an article entitled Eigenvalue Assignment of Two-dimensional Systems Using PID Controllers&#34;, in International Journal of Systems Science, Vol. 12, No. 4, pages 407-422, and Tzafestas and Pimenides in 1983, in a paper entitled &#34;Feedback Characteristic Polynomial Controller Design of 3-D Systems in State-space&#34;, from the Control System Laboratory, School of Engineering, University of Patras, Patras, Greece, Vol. 314, No. 3, pages 169-189. Paraskevopoulos et al presented a method to reassign the poles of a two-dimensional system to a set of desired values using a static state-feedback or static output feedback controller, Paraskevopoulos et al developed a multidimensional proportional, integral, derivative (PID) controller for controlling the poles of the system, and Tzafestas et al extended the concept of poles assignment by feedback controllers to the three-dimensional systems. The difficulty associated with the implementation of these methods is in knowing or determining the poles and the eigenvalues of the state-space model of the distributed-parameter system. 
     The prior art includes an optimal control technique as illustrated by Sage and White, in 1977, in a book entitled Optimum Systems Control (New Jersey: Prentice-Hall Inc.), and by Kou in 1980, in a book entitled Digital Control Systems (New York: Holt, Rinehart and Winston Inc.). 
     SUMMARY OF THE INVENTION 
     The present invention uses an optimal distributed-control technique based on an extension of the single-dimensional linear quadratic Gaussian technique as illustrated by Sage and White, and by Kou in 1980, in a book entitled Digital Control Systems (New York: Holt, Rinehart and Winston Inc.). The method requires a performance criterion be defined and the parameters of the system be known. The goal is to determine an optimal controller such that the performance criterion is minimized. The control gains may vary with respect to time and/or space. When this is the case, the optimal controller is referred to as a spatial-time varying optimal controller whereas for the case of constant gains the controller is said to be in steady-state. The steady-state gains of an N-dimensional discrete optimal controller are computed by a set of N th  -order polynomials. For example, the steady-state gains of a two-dimensional discrete optimal controller are computed from a set of fourth-order polynomials and it is called the optimal quartic controller. A number of theorems are presented to support the design procedure. The theorems are directly extended from the single-dimensional systems as illustrated by Kou to multidimensional systems. 
     My invention is particularly applicable to a control system that uses a model of a linear continuous distributed parameter system which, when excited by a stimulus, produces both desired and undesired output responses. Thus, it is desired to use the linear system in order to obtain its desired responses, but the undesired responses are objectionable and would be eliminated if possible. The undesired responses act on some physical body in an objectionable way. For example, consider the walls and motor of a refrigerator as a linear continuous distributed parameter system. The motor has the desired response of compressing refrigerant to operate the refrigerator, but, responsive to the motor, the walls have the undesired response of vibrating, which makes objectionable noise in the house in which the refrigerator is located. The transfer function of the linear system between the stimulus (for example, the power applied to the refrigerator motor) and the undesired response (for example, the vibrations which cause noise in the refrigerator walls) is converted to a state-space model of the transfer function and as such is used to govern the choices of possibilities to be made by a digital computer. The digital computer responds to the stimulus which excites the linear system to determine from the space-state model the possible responses for the linear system. Through choice-optimization logic, it derives an optimum canceling response and uses that derived optimum response to control a transducer which applies a derived physical response to the physical body to cancel the undesired output. For example, a time-coincident noise of equal amplitude and opposite polarity to the undesired noise being applied to the refrigerator walls can be applied to those walls to cancel the undesired output response. 
     The conversion to a distributed state-space model can be done by describing the continuous distributed transfer function of the linear distributed system using a quasi-Laplace or Moshfegh transform format, then applying a quasi-Z or Chester transform to the quasi-Laplace or Moshfegh transform format to derive a digital transfer distributed transfer function, and then converting the digital transfer function into a distributed state-space model. These are called quasi-Laplace (or Moshfegh) and quasi-Z (or Chester) formats because, unlike the strictly traditional Laplace and Z transforms which operate on differential equations involving only one independent variable, typically a time functions, the quasi functions may operate on partial differential equations involving a plurality of independent variable, typically both time and spatial dimensions. Otherwise, the mathematics is similar to that involved with the strictly traditional forms of the formats. A quasi-Laplace transform, also called a Moshfegh transform, is defined as a transform which is mathematically in the nature of a Laplace transform except that, whereas a Laplace transform involves time dimensions as the only independent variable involved in the transform, a quasi-Laplace transform or Moshfegh transform uses space dimensions as an independent variable in at least one (but not all) of the places where a Laplace transform would use time dimensions. A quasi-Z transform, also called a Chester transform, is defined as a transform which is mathematically in the nature of a Z-transform except that, whereas a Z-transform involves time dimensions as the only independent variable involved in the transform, a quasi-Z transform or Chester transform uses space dimensions as an independent variable in at least one (but not all) of the places where a Z-transform would use time dimensions. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS 
     FIGS. 1 and 1A provide an overall block diagram of a preferred embodiment of the invention. 
     FIG. 2 is a block diagram of the structure of a two-dimensional discrete space-state model and an optimal distributed control system. 
     FIG. 3 is a diagram showing two-dimensional propagation of the weighting factors P h  (i,j) and P v  (i,j). 
     FIG. 4 is a signal-flow diagram of a distributed parameter system. 
     FIG. 5 is a graph showing uncontrolled spatial responses of a distributed parameter system for various time samples. 
     FIG. 6 is a graph showing uncontrolled time responses of a distributed parameter system for various spatial samples. 
     FIG. 7 is a graph of overall uncontrolled spatial-time response of a distributed-parameter system. 
     FIG. 8 is a graph of the overall spatial-time propagation of the weighting factor P h  (i,j). 
     FIG. 9 is a graph of the overall spatial-time propagation of the weighting factor P v  (i,j). 
     FIG. 10 is a graph of the overall spatial-time propagation of the weighting factor Q 2  (i,j). 
     FIG. 11 is a graph of the overall spatial-time variation of the optimal control gain G h  (i,j). 
     FIG. 12 is a graph of the overall spatial-time variation of the optimal control gain G v  (i,j). 
     FIG. 13 is a graph of optimally controlled time responses of the distributed parameter system for various spatial samples. 
     FIG. 14 is a graph of optimally controlled spatial responses of the same system for various time samples. 
     FIGS. 15 and 16 are different views of a three-dimensional graph showing overall optimally controlled spatial-time response of the distributed parameter system. 
    
    
     DESCRIPTION OF THE PREFERRED EMBODIMENT 
     FIG. 1 provides an overall schematic view of the preferred embodiment of the invention. An excitable system 10, which is a linear continuous distributed parameter system, is excited by a stimulus 12 to produce a desired output 14 and an undesired output 16. The continuous transfer function 18 of the excitable system is derived by prior art methods and techniques discussed herein are used by a converter 20 to convert this continous transfer function into a representation 22 of a space-state model 26 of the transfer function for use in a digital computer as the space-state model upon which it operates as a digital controller. The undesired output 16 adversely affects a physical body 30. Digital computer 24, under the control of computation control logic 32, operates a choice optimization logic section 34 to choose an optimum derived output 36 from those family of outputs 37 available from state-space model 26. The optimum derived output is applied to a transducer 38, which applies a cancellation output response to the physical body 30, thereby canceling the effects of the undesired output 16. 
     FIG. 1A shows a further breakdown of converter 20, showing that it operates, preferably off-line at an earlier time than the rest of the control function illustrated in FIG. 1, by converting a distributed parameter model of system 10 into a quasi-Laplace transform format in a first converter 40, converting the quasi-Laplace transform version to a quasi-Z transform format in a second converter 42, and finally converting the quasi-Z transform format in a third converter 44 into the solid-space model. In practice, these various converters are sequential uses of a digital computer under the control of the equations appropriate for the particular conversion. 
     Most physical processes typically fall into one of the following classes of partial differential equations: hyperbolic, parabolic, or elliptic. The hyperbolic class of differential equations originates from vibratory systems, structural or fluid-borne acoustic noises and shock waves. Heat conduction and unsteady-state heat flow processes are described by the parabolic class of differential equations. The steady-state potential distribution or temperature equilibrium problems are generally associated with the elliptic class. 
     The distributed-parameter system considered in this section is described by a generalized second-order partial differential equation with respect to two independent variables x (spatial variable), and t (time variable) and is presented by: ##EQU1## where a, b, c, d, e, f, and g may also be functions of the independent variables x and t. Depending on the values of the coefficients of the second partial derivative terms,-system (1) is classed as hyperbolic when (b 2  -4ac)&gt;0, parabolic when (b 2  -4ac)=0, and elliptic when (b 2  -4ac)&lt;0. Distributed-parameter system (1) is transformed into a state-space model based on a technique discussed in my doctoral dissertation, cited above. The summary of the procedure is as follows: 
     a. Apply the multidimensional Laplace-like transformation to the dynamic equation (1). 
     b. Eliminate any term that contains a zero as an argument. 
     c. Derive the continuous multidimensional transfer function. 
     d. Convert the continuous transfer function to a discrete one using the multidimensional bilinear transformation. 
     e. Realize a multidimensional state-space model from the discrete transfer function. 
     This gives the following two-dimensional discrete state-space model: ##EQU2## The compact form is given by: 
     
         X.sup.11 (i,j)=Φ X(i,j)+Ψ u(i,j)                   (2b) 
    
     
         y(i,j)=ΛX(i,j)+D u(i,j)                             (3b) 
    
     where x h  (i,j)εR n  and x v ΕR m  are the state vectors, u(i,j)εR p  is the input or the external force vector acting on the system, and y(i,j)εR q  is the system response. Matrices A 1 , A 2 , A 3 , A 4 , B 1 , B 2 , C 1 , C 2 , and D are of appropriate dimensions, real and constant. The continuous initial and boundary conditions of the dynamic system (1) must also be converted to a set of conditions in terms of the state variables of the multidimensional discrete model (2a). Thus the initial and boundary conditions are presented by the following two equations: ##EQU3## respectively. To control the dynamics of two-dimensional system (2a)-(3a) in an optimal manner it is necessary to determine an optimal control law such that it minimizes a desired performance criterion and to influence the dynamics of the state variables x h  (i,j) and x v  (i,j) in some desired fashion. However, the system must satisfy certain requirements. For instance, the system must be at least locally controllable which means that all the states can be influenced by the control law, and the control law itself must be at least locally observable. 
     The objective is to define a meaningful performance criterion by which a distributed-parameter system is controlled. To determine such a performance criterion, a loss function must be defined. A loss function describes losses incurred when the actual states or the outputs of the system are not equal to the prescribed states or the prescribed outputs. A loss function may be formulated in terms of the state variables x h  (i,j) and x v  (i,j), the control function u(i,j) and system&#39;s response y(i,j). Note, x h  (i,j) is denoted by x h  (i,j=1,2, . . . ; x h  (0,j); x v  (i,0); u(i,j)), similarly x v  (i,j) is denoted by x v  (i,j=1,2, . . . ; x h  (0,j); x v  (i,0); u(i,j)). The following are some possible loss functions: 
     
         L.sub.1 (i,j)=∥X(i,j)∥.sup.2.sub.Q(i,j) +∥u(i,j)∥.sup.2.sub.x +X(i,j) &#39;Su(i,j)+[Su(i,j)]&#39;X(i,j) 
    
     
         L.sub.2 (i,j)=∥y(i,j)∥.sup.2.sub.Z +∥u(i,j).sup.2.sub.R +y(i,j)&#39;Su(i,j)+[Su(i,j)]&#39;y(i,j) 
    
     
         L.sub.3 (i,j)=∥u(i,j)∥.sup.2.sub.R 
    
     Matrix Q(i,j) is of dimension [(n+m), (n+m)], nonnegative definite, symmetric and real; matrix R is of dimension [p,p], positive definite, symmetric, real and constant; matrix S is of dimension [(n+m),p], real and constant; matrix Z is of dimension [q,q], nonnegative definite, symmetric, real and constant. Also Q(i,j)-SS&#39; and Z-SS&#39; are nonnegative definite. A performance criterion based on loss function L 1  (i,j) is presented by: ##EQU4## where 0≦N≦∞ and 0≦M≦∞. The next step is to determine the optimal control law. 
     Procedures and theorems of optimization for optimum performance of a single-dimensional system are well defined, Sage (1977). One optimization technique is by Pontryagin&#39;s maximum principle. This technique sets up a differential equation known as the Hamilton-Jacobi equation. From the Hamilton-Jacobi equation an optimal control law can be derived that minimizes the performance criterion. This concept can be extended for the optimization of a multidimensional performance criterion, as will be seen below. The extension of Pontryagin&#39;s maximum principle is considered for the two-dimensional performance criterion (6) but the procedure can easily be extended to higher dimensional performance criteria. 
     CASE 1. 
     Time-Spatial Varying Optimal Control Gain 
     Problem Statement: Find an optimum control law u o  (i,j) such that the performance criterion: ##EQU5## is minimized, where J 1  is subjected to the two-dimensional system constraint (2a). The following is the outline of the two-dimensional optimization procedure: 
     Step 1: Define a two-dimensional function H(i,j). H(i,j) defines the augmentation of system constraint (2a) and the loss function L 1  by two costate vectors λ(i,j)εR n  and γ(i,j)εR m . Costates λ(i,j) and γ(i,j) correspond to the state variables x h  (i,j) and x v  (i,j), respectively. Function H(i,j) is named the two-dimensional Hamiltonian function and is denoted by: ##EQU6## where, ##EQU7## Q 1  is [n,n], Q 4  is [m,m], s 1  is [n,p], S 2  is [m,p], and R is [p,p]. Matrices Q 1  and Q 4  must be positive definite or semidefinite, symmetric, real, and constant; matrix R must be positive definite, symmetric, real, and constant; matrices Q 1-S   1  S 1  &#39; and Q 4  -S 2  S 2  &#39; must be at least nonnegative real and constant. Performance criterion J 1  is considered minimized if the two-dimensional Hamiltonian function, H(i,j), has an absolute minimum with respect to the optimum control function, u 0  (i,j), for i,j=0, 1, 2, . . . over the space of all admissible control functions u(i,j). Let: 
     
         H(I,J) ΔH(X(i,j); λ(i,j); γ(i,j); u(i,j); Q(i,j); R; S; i,j) 
    
     then, 
     
         H(X.sup.0 ; λ.sup.0 ; γ.sup.0 u.sup.0 ; i,j)≦H(X.sup.o ; λ.sup.o ; γ.sup.0 ; u; i,j) 
    
     Step 2: Establish the necessary conditions for the optimization. The first set of necessary conditions are with respect to the state variables: ##EQU8## and the second set are with respect to the costates: ##EQU9## The final necessary condition is with respect to u(i,j): ##EQU10## 
     Step 3: Assume a generalized solution for the costate vectors by defining a two-dimensional function Ω(i,j) that is a Lyapunov candidate function. This is presented by: 
     
         Ω(i,j)=0.5 ∥X(i,j) ∥.sup.2.sub.P(i,j) (14) 
    
     where, ##EQU11## Weighting matrices P h  (i,j) and P v  (i,j) are [n,n] and [m,m], respectively, and they are diagonal, symmetric and real. General solutions of the costate vectors are: ##EQU12## 
     Step 4: Formulate the optimal control law u 0  (i,j) by using equations (9) to (15). This gives: ##EQU13## The compact form is presented by: 
     
         u.sup.o (i,j)=-G(i,j) X(i,j)                               (16b) 
    
     where, 
     
         G(i,j)=Γ(i,j) (S&#39;+Ψ&#39;P.sup.11 (i,j) Φ)        (17) 
    
     
         Γ(i,j)=(R+Ψ&#39;P.sup.11 (i,j) Ψ).sup.-1         (18) 
    
     and, ##EQU14## 
     From the optimal gain G(i,j), it can be observed that the requirement of the positive definiteness for matrix R can be removed since the inverse of R is no longer required. However, Γ(i,j) must exist. The optimal control law u o  (i,j) is a two-dimensional time-spatial varying controller and is referred to as Optimal Quartic Controller. FIG. 2 illustrates the two-dimensional discrete state-space model along with the optimal quartic controller. 
     Step 5: Compute weighting matrices P h  (i,j) and P v  (i,j). Substitute equations (12), (15b) and (16b) into equation (10) then eliminate X(i,j) from both sides of the equation. This gives: 
     
         P(i,j)=Φ&#39;P.sup.11 (i,j) (Φ-ΨG(i,j))-S G(i,j)+Q(i,j) (19) 
    
     Equation (19) is named Linear Quartic Regulator (LQR). LQR is computed recursively by backward iteration in terms of P h  (iΔ,jΔ), P v  (iΔ,jΔ) and Q 2  (iΔ,jΔ) starting from the known weighting matrix P(N,M). P(N,M) is defined in the performance criterion J 1  (7): ##EQU15## 
     Matrices P h  (N,M) and P v  (N,M) are positive definite, symmetric, real and constant. FIG. 3, presents the two-dimensional backward propagation of the weighting matrices P h  (i,j) and P v  (i,j) starting from the time-spatial point (N,M). LQR has (n+m) 2  equations and the total number of unknown parameters in matrices P h , P v  and Q 2  are given by the following relationship: ##EQU16## 
     Case 2. Steady-State or Time-Spatial Invariant Optimal Control Gain: 
     In this case the performance criterion contains infinite summations with respect to independent variables. For example, considering performance criterion (7) for N=∞ and M=∞. This gives: ##EQU17## 
     Weighting matrices P h  (N,M) and P v  (N,M) are selected to be null matrices when (N,M)=(∞,28 ). The reason is that their values have no impact on the final result of matrix P. The optimization procedure is similar to the previous case and the system (the state-space model) must be locally controllable and observable. As (i,j)→(∞,∞) the optimal control gain G(i,j), the weighting matrices P (i,j) and Q 2  (i,j) become constant: ##EQU18## 
     Therefore, the steady-state optimal quartic control law is presented by: ##EQU19## where, 
     
         G.sub.ss Δ[G.sub.ss.sup.h G.sub.ss.sup.v ]=(R+Ψ&#39;P.sub.ss Ψ).sup.-1 (S&#39;+Ψ&#39;P.sub.ss Φ)                   (25) 
    
     Weighting matrix P ss  is determined by solving the following simultaneous quartic matrix equation: 
     
         P.sub.ss =Φ&#39;P.sub.ss (Φ-ΨG.sub.ss)-S G.sub.ss +Q.sub.ss (26) 
    
     where, ##EQU20## 
     Theorem 1: Weighting matrices P h  (i,j) and P v  (i,j) are symmetric. 
     Proof: Let matrices R and Q(i,j) be symmetric. Matrix transpose of LQR (19) is: 
     
         P&#39;(i,j)=(Φ&#39;-G&#39;(i,j)Ψ&#39;)P.sup.11&#39; (i,j)Φ-G&#39;(i,j)S&#39;+Q&#39;(i,j) 
    
     The only difference between equation above and equation (19) matrices P(i,j) and P 11  (i,j) are replaced by P&#39;(i,j) and P 11&#39;  (i j) respectively. The two equations produce the same results. This implies P(i,j)=P&#39;(i,j) and P 11  (i,j)=P 11&#39;  (i,j). In other words, matrices P(i,j) and P 11  (i,j) are symmetric. 
     Theorem 2: Given a performance criterion described by: ##EQU21## and subjected to the distributed-parameter system (2a). The optimum performance criterion for the case when 0≦M&lt;∞ and 0≦N&lt;∞ is: ##EQU22## and for the case when M=∞ and N=∞ is: ##EQU23## 
     Theorem 3: Performance criterion J is described by: ##EQU24## If matrices Q ss  and R are positive definite, real and symmetric, then weighting matrix P ss  is positive definite. 
     Proof: Since matrices Q ss  and R are positive definite, then performance criterion (31) is positive. The reason is that the performance criterion J is a quadratic function of the state variables and the control law. From theorem 2, J is presented by the quadratic relation (29). Using equation (29) and knowing that J is positive then P ss  must be positive definite. 
     The optimal state and output responses of the system are presented by: ##EQU25## respectively. 
     Theorem 4: Consider performance criterion (31) under constraint of the controllable distributed-parameter system (2a)-(3a) where weighting matrices Q ss  and R are positive definite, real and symmetrical. The initial state variables x h  (0,0) and x v  (0,0) are observable and the optimal quartic controller that minimizes the performance criterion is: ##EQU26## where, 
     
         [G.sub.ss.sup.h G.sub.ss.sup.v ]=(R+Ψ&#39;P.sub.ss Ψ).sup.-1 Ψ&#39;P.sub.ss Φ                                      (34) 
    
     and 
     
         P.sub.ss =Φ&#39;P.sub.ss (Φ-ΨG.sub.ss)+Q.sub.ss    (35) 
    
     Then the closed-loop optimal feedback system: ##EQU27## is asymptotically stable. 
     Proof: By theorem 3, weighting matrix P ss  is positive definite if weighting matrices Q ss  and R are positive definite. Define a two-dimensional discrete Lyapunov candidate function: 
     
         V[X(i,j)] ΔV[x.sup.h (i,j); x.sup.v (i,j); i,j]=0.5∥X(i,j)∥.sup.2.sub.P.sbsb.se    (37) 
    
     The Lyapunov candidate function V has the following properties: 
     a) V[X(i,j)] is a positive valued function for all x h  (i,j)≠0 and x v  (i,j)≠0 
     b) V[X(i,j)] is zero for x h  (i,j)=0 and x v  (i,j)=0 
     c) V[X(i,j)] approaches infinity as ∥ X(i,j) ∥→∞ 
     Let, 
     
         V[X.sup.11 (i,j)]=V.sup.h [x.sup.h (i+1,j); x.sup.v (i,j)]+V.sup.v [x.sup.h (i,j); x.sup.v (i,j+1)] 
    
     then, 
     
         ΔV[X(i,j)]=V[X.sup.11 (i,j)]-V[X(i,j)]               (38) 
    
     or, 
     
         ΔV[X(i,j)]=0.5 (∥X.sup.11 (i,j)∥.sup.2.sub.P.sbsb.ss -∥X(i,j)∥.sup.2.sub.P.sbsb.ss)          (39) 
    
     If ΔV[X(i,j)]&lt;0 then the closed-loop system (36) is asymptotically stable. Substitute equation (36) into equation (39). This gives: 
     
         ΔV[X(i,j)]=0.5X&#39;(,j)((Φ-ΨG.sub.ss)&#39;P.sub.ss (Φ-ΨG.sub.ss)-P.sub.ss))X(i,j)                    (40) 
    
     Substitute the right hand side of equation (35) into equation (40). This gives the following reduced form relation: 
     
         ΔV[X(i,j)]=-0.5 X&#39;(i,j) (Q+Y)X(i,j)                  (41) 
    
     where, 
     
         Y=G&#39;.sub.ss Ψ&#39;P.sub.ss (Φ-ΨG.sub.ss)           (42) 
    
     From equation (34) the following identity holds: 
     
         Ψ&#39;P.sub.ss Φ=(R+Ψ&#39;P.sub.ss Φ)G.sub.ss      (43) 
    
     Then Y is reduced to the following relationship by substituting equation (43) into equation (42): 
     
         Y=G&#39;.sub.ss R G.sub.ss =∥G.sub.ss ∥.sub.R.sup.2 (44) 
    
     Matrices Q ss  and P ss  are positive definite and matrix Y is nonnegative definite because R is positive definite. Therefore, ΔV[(X(i,j)] is nonpositive, V[X(i,j)] is a two-dimensional Lyapunov function and the closed-loop system (36) is asymptotically stable. 
     The performance of the optimal distributed-control system can be demonstrated by considering the dynamics of noise (acoustic noise) in a combustion chamber. The linearized equation of motion for such a process is described by the following second-order continuous hyperbolic differential equation: ##EQU28## for t&gt;0 and 0&lt;x&lt;L, where is L the length of the chamber. The boundary and initial conditions are given by: 
     
         y(0,t)=W(t); 0≦t                                    (46) 
    
     and 
     
         y(x,0)=F(x); 0≦x≦L                           (47) 
    
     respectively. The state-space model of the distributed-parameter system (45) is obtained by implementing the steps outlined in section 2.1. The steps are: 
     Step 1, multidimensional Laplace-like transformation: 
     
         L.sub.x L.sub.t [Model(45)]=a.sub.1 (s.sub.1 s.sub.2 Y(s.sub.1,s.sub.2)-s.sub.2 Y(0,s.sub.2) 
    
     
          -s.sub.1 Y(s.sub.1,0)+Y(0,0))+a.sub.2 (s.sub.2 Y(s.sub.1,s.sub.2)-Y(s.sub.1,0))+a.sub.3 (s.sub.1 Y(s.sub.1,s.sub.2 
    
     
          -Y(0,s.sub.2))+a.sub.4 Y(s.sub.2,s.sub.2)=bU(s.sub.1,s.sub.2) (48) 
    
     Step 2, eliminate terms with zero as the argument in equation (48): 
     
         [a.sub.1 s.sub.1 s.sub.2 +a.sub.2 s.sub.2 +a.sub.3 s.sub.1 +a.sub.4 ]Y(s.sub.1,s.sub.2)=bU(s.sub.1,s.sub.2)                   (49) 
    
     Step 3, derive the continuous multidimensional transfer function: ##EQU29## 
     Step 4, convert transfer function (50) to a discrete transfer function using bilinear transformation: ##EQU30## where, Δx and Δt are sampling intervals with respect to space and time. 
     Step 5, realize the discrete transfer function (51) into a state-space model, see FIG. 4. The state-space model is presented by: 
     
         X.sup.11 (iΔx,jΔt)=ΦX(iΔx,jΔt)+Ψu(iΔx,j.DELTA.t)                                                        (52) 
    
     
         y(iΔx,jΔt)=ΔX(iΔx,jΔt)+D u(iΔx,jΔt)                                    (53) 
    
     where, 
     where, ##EQU31## and, 
     
         r=a.sub.1.sup.-1 (4a.sub.1 +2a.sub.3 Δx+2a.sub.2 Δt+a.sub.4 ΔxΔt) 
    
     Consider the case when coefficients of the distributed-parameter system are a 1  =a 2  =a 3  =1, a 4  =-1, and b=1. The initial condition is defined as W(t)=10 e -t , the boundary condition is constant and is given by F(x)=10 and the length of the chamber is normalized to unity. The discrete approximation of the initial and boundary conditions are: 
     
         y(Δx,0)=10e.sup.-iΔx for L=MΔx=1; i=0, 1, 2, . . . , M (54) 
    
     and, 
     
         y(0,jΔt)=10 for j=0, 1, 2, . . .                     (55) 
    
     respectively. The sampling intervals with respect to time and space are Δt=0.1 and Δx=0.05, respectively. The spatial and time responses of the system are shown in FIGS. 5 and 6, respectively. The three-dimensional distributed motion is shown in FIG. 7. In order to control the noise in the chamber the following performance criterion: ##EQU32## is minimized. The weighting matrices are: ##EQU33## where Q 2  (iΔ,jΔ) to be determined by a LQR relationship, and ##EQU34## 
     An LQR relation similar to equation (19) is written in terms of the state-space model (52) and the above weighting matrices. This gives: ##EQU35## The optimal gains are determined by: ##EQU36## where, 
     
         Γ(iΔx,jΔt)=(1+0.0542[P.sup.h ((i+1)Δx,jΔt) P.sup.v (iΔx, (j+1)Δt)]).sup.-1 
    
     LQR equation (57) is computed recursively by a backward iteration in terms of P h  (iΔ,jΔ), P v  (iΔ,jΔ) and Q 2  (iΔ,jΔ). Computation starts by the following initial values for the weighting matrix P(iΔx,jΔt): 
     P h  ((i+1)Δx,jΔt)=0 for i=21 and j=0, 1, . . . , 51 
     P h  (20Δx,50Δt)=1 
     P v  (iΔx,(j+1)Δt)=0 for j=51 and i=0, 1, . . . , 21 
     P v  (20Δx,50Δt)=1 
     and, 
     G(iΔx,jΔt)=0 for i=0, 1, . . . , 21 and j=51; i=21 and j=0, 1, . . . , 51; (i,j)=(20,50) 
     FIGS. 8, 9 and 10 illustrate the time and spatial variations of weighting matrices P h  (iΔ,jΔ), P v  (iΔ,jΔ) and Q 2  (iΔ,jΔ) by a three-dimensional plot where one axis presents the time, another axis presents the space and the third axis gives the value of the weighting matrix, respectively. The time and spatial responses for the optimal gains G h  (iΔx,jΔt) and G v  (iΔx,jΔt) are illustrated by three-dimensional plots, see FIGS. 11 and 12. The optimal gains G h  (iΔx,jΔt) and G v  (iΔx,jΔt) will reach their steady-state values within 8 to 12 iterations with respect to each independent variable. The steady-state values of the optimal gains G h  (iΔx,jΔt) and G v  (iΔx,jΔt) are denoted by G h   ss  and G v   ss  and their values are 1.1976 and 5.0699, respectively. The optimal controlled response of the distributed-parameter system (52)-(53) using the optimal quartic controller G(iΔx,jΔt) as the state-feedback controller are shown in FIGS. 13, 14, 15 and 16. FIGS. 15 and 16 show the same graph, but for the sake of clarity, the view shown in FIG. 16 is rotated 60 degrees to the right in azimuth and down 15 degrees in elevation from the view show in FIG. 15. 
     APPENDIX B 
     This appendix contains a copy of a Fortran program which operates to carry out a number of the calculations in accordance with the invention. They are included for the convenience of the Examiner, and will not be included in the specification unless their inclusion is requested or recommended. ##SPC1##