ON EIGENVALUE BOUNDS AND ITERATION METHODS FOR DISCRETE ALGEBRAIC RICCATI EQUATIONS

We derive new and tight bounds about the eigenvalues and certain sums of the eigenvalues for the unique symmetric positive definite solutions of the discrete algebraic Riccati equations. These bounds considerably improve the existing ones and treat the cases that have been not discussed in the literature. Besides, they also result in completions for the available bounds about the extremal eigenvalues and the traces of the solutions of the discrete algebraic Riccati equations. We study the fixed-point iteration methods for com- puting the symmetric positive definite solutions of the discrete algebraic Riccati equations and establish their general convergence theory. By making use of the Schulz iteration to partially avoid computing the matrix inversions, we present effective variants of the fixed-point iterations, prove their monotone convergence and estimate their asymptotic convergence rates. Numerical results show that the modified fixed-point iteration methods are feasible and effective solvers for computing the symmetric positive definite solutions of the discrete algebraic Riccati equations.

Doubling Algorithm for Nonsymmetric Algebraic Riccati Equations Based on a Generalized Transformation

We consider computing the minimal nonnegative solution of the nonsymmetric algebraic Riccati equation with M-matrix.It is well known that such equations can be efficiently solved via the structure-preserving doubling algorithm(SDA)with the shift-and-shrink transformation or the generalized Cayley transformation.In this paper,we propose a more generalized transformation of which the shift-and-shrink transformation and the generalized Cayley transformation could be viewed as two special cases.Meanwhile,the doubling algorithm based on the proposed generalized transformation is presented and shown to be well-defined.Moreover,the convergence result and the comparison theorem on convergent rate are established.Preliminary numerical experiments show that the doubling algorithm with the generalized transformation is efficient to derive the minimal nonnegative solution of nonsymmetric algebraic Riccati equation with M-matrix.

THE SOLUTION FOR THE GENERALIZED RICCATIALGEBRAIC EQUATIONS OF LINEAR EQUALITY CONSTRAINT SYSTEM

Based on the dynamic equation, the performance functional and the system constraint equation of time-invariant discrete LQ control problem, the generalized Riccati equations of linear equality constraint system are obtained according to the minimum principle, then a deep discussion about the above equations is given, and finally numerical example is shown in this paper.

Secure Synchronization Control for a Class of Cyber-Physical Systems With Unknown Dynamics

This paper investigates the secure synchronization control problem for a class of cyber-physical systems(CPSs)with unknown system matrices and intermittent denial-of-service(DoS)attacks.For the attack free case,an optimal control law consisting of a feedback control and a compensated feedforward control is proposed to achieve the synchronization,and the feedback control gain matrix is learned by iteratively solving an algebraic Riccati equation(ARE).For considering the attack cases,it is difficult to perform the stability analysis of the synchronization errors by using the existing Lyapunov function method due to the presence of unknown system matrices.In order to overcome this difficulty,a matrix polynomial replacement method is given and it is shown that,the proposed optimal control law can still guarantee the asymptotical convergence of synchronization errors if two inequality conditions related with the DoS attacks hold.Finally,two examples are given to illustrate the effectiveness of the proposed approaches.

Application of Neurocomputing in Adaptive Control of Large-Scale Aerospace Systems

We are engaged in solving two difficult problems in adaptive control of the large-scale time-variant aerospace system. One is parameter identification of time-variant continuous-time state-space modei; the other is how to solve algebraic Riccati equation (ARE) of large order efficiently. In our approach, two neural networks are employed to independently solve both the system identification problem and the ARE associated with the optimal control problem. Thus the identification and the control computation are combined in closed-loop, adaptive, real-time control system . The advantage of this approach is that the neural networks converge to their solutions very quickly and simultaneously.

New algorithm for robust H_2/H_∞ filtering with error variance assignment

We consider the robust H 2/H ∞ filtering problem for linear perturbed systems with steadystate error variance assignment. The generalized inverse technique of matrix is introduced, and a new algorithm is developed. After two Riccati equations are solved, the filter can be obtained directly, and the following three performance requirements are simultaneously satisfied: The filtering process is asymptotically stable; the steadystate variance of the estimation error of each state is not more than the individual prespecified upper bound; the transfer function from exogenous noise inputs to error state outputs meets the prespecified H ∞ norm upper bound constraint. A numerical example is provided to demonstrate the flexibility of the proposed design approach.

Feedback Stackelberg Solution for Mean-Field Type Stochastic Systems with Multiple Followers

This paper discusses feedback Stackelberg strategies for the continuous-time mean-field type stochastic systems with multiple followers in infinite horizon.First,optimal control problems of the followers are studied in the sense of Nash equilibrium.With the help of a set of generalized algebraic Riccati equations(GAREs),sufficient conditions for the solvability are put forward.Then,the leader faces a constrained optimal control problem by transforming the cost functional into a trace criterion.Employing the Karush-Kuhn-Tucker(KKT)conditions,necessary conditions are presented in term of the solvability of the cross-coupled stochastic algebraic equations(CSAEs).Moreover,feedback Stackelberg strategies are obtained based on the solutions of the CSAEs.In addition,an iterative scheme is introduced to obtain efficiently the solutions of the CSAEs.Finally,an example is given to shed light on the effectiveness of the proposed results.

Adaptive dynamic programming for online solution of a zero-sum differential game

This paper will present an approximate/adaptive dynamic programming(ADP) algorithm,that uses the idea of integral reinforcement learning(IRL),to determine online the Nash equilibrium solution for the two-player zerosum differential game with linear dynamics and infinite horizon quadratic cost.The algorithm is built around an iterative method that has been developed in the control engineering community for solving the continuous-time game algebraic Riccati equation(CT-GARE),which underlies the game problem.We here show how the ADP techniques will enhance the capabilities of the offline method allowing an online solution without the requirement of complete knowledge of the system dynamics.The feasibility of the ADP scheme is demonstrated in simulation for a power system control application.The adaptation goal is the best control policy that will face in an optimal manner the highest load disturbance.

Infinite horizon indefinite stochastic linear quadratic control for discrete-time systems

This paper discusses discrete-time stochastic linear quadratic （LQ） problem in the infinite horizon with state and control dependent noise, where the weighting matrices in the cost function are assumed to be indefinite. The problem gives rise to a generalized algebraic Riccati equation （GARE） that involves equality and inequality constraints. The well-posedness of the indefinite LQ problem is shown to be equivalent to the feasibility of a linear matrix inequality （LMI）. Moreover, the existence of a stabilizing solution to the GARE is equivalent to the attainability of the LQ problem. All the optimal controls are obtained in terms of the solution to the GARE. Finally, we give an LMI -based approach to solve the GARE via a semidefinite programming.

Mixed deterministic and random optimal control of linear stochastic systems with quadratic costs

In this paper,we consider the mixed optimal control of a linear stochastic system with a quadratic cost functional,with two controllers—one can choose only deterministic time functions,called the deterministic controller,while the other can choose adapted random processes,called the random controller.The optimal control is shown to exist under suitable assumptions.The optimal control is characterized via a system of fully coupled forward-backward stochastic differential equations(FBSDEs)of mean-field type.We solve the FBSDEs via solutions of two(but decoupled)Riccati equations,and give the respective optimal feedback law for both deterministic and random controllers,using solutions of both Riccati equations.The optimal state satisfies a linear stochastic differential equation(SDE)of mean-field type.Both the singular and infinite time-horizonal cases are also addressed.

Infinite horizon H-two/H-infinity control for descriptor systems:Nash game approach

This paper studies the infinite time horizon mixed H-two/H-infinity control problem for descriptor systems using Nash game approach. A necessary/sufficient condition for the existence of infinite horizon H-two/H-infinity control is presented in the form of two coupled algebraic Riccati equations, respectively. Finally, a suboptimal H-two/H-infinity controller design is given based on an iterative linear matrix inequality algorithm.

An Infinite Horizon Linear Quadratic Problem with Unbounded Controls in Hilbert Space

An infinite horizon linear quadratic optimal control problem for analytic semigroup with unbounded control in Hilbert space is considered.The state weight operator is allowed to be inddefinite while the control weight operator is coercive.Under the exponential stabilization condition,it is proved that any optimal control and its optimal trajectory are continuous.The positive real lemma as a necessary and sufficient condition for the unique solvability of this problem is established.The closed-loop synthesis of optimal control is given via the solution to the algebraic Riccati equation.

Static-feedback guaranteed cost control for linear systems with outage and loss of effectiveness actuator faults

This paper investigates the static-feedback guaranteed cost control problem for linear systems with actuator faults including outage and loss of effectiveness.Under the actuator redundancy condition,theoretical analysis shows that a static-feedback guaranteed cost controller can always be well designed to ensure that the resulting closed-loop system is stable with desirable quadratic performance.In particular,the feedback gain can be determined through the solution of a modified algebraic Riccati equation.Furthermore,extension to the system with uncertainties is further studied.Compared with the dynamic feedback controller,the static-feedback controller consists only of logical gates/modules and it does not require any memory element,and hence it is simplest in a design perspective.Different from the existing results,the severe and timevarying actuator outage faults can be handled very well by the proposed control strategy.Finally,simulation on a linearised reduced-order aircraft system is provided for verifying the theoretical results.