H ∞ Optimal control

Our aim in this paper is to develop a new approach for solving the H ^∞ optimal control problem where the feedback arrangement takes the form of a linear fractional transformation. The paper is in two parts. In Part 1, a basic kind of model-matching problem is considered: given rational matrices M(s) and N(s), the H ^∞-norm of an error function defined as E(s)=M(s) – N(s)Q(s) is minimized (or bounded) subject to E(s) and Q(s) being stable. Closed-form state-space characterizations are obtained for both E(s) and Q(s). The results established here will be used in Part 2 of the paper (Hung 1989) to solve the H ^∞ optimal control problem.     

H2/H∞ control for stochastic systems with delay

This paper is concerned with the H₂/H_∞ control problem for stochastic linear systems with delay in state, control and external disturbance-dependent noise. A necessary and sufficient condition for the existence of a unique solution to the control problem is derived. The resulting solution is characterised by a kind of complex generalised forward–backward stochastic differential equations with stochastic delay equations as forward equations and anticipated backward stochastic differential equations as backward equations. Especially, we present the equivalent feedback solution via a new type of Riccati equations. To explain the theoretical results, we apply them to a population control problem.     

Stabilisation and H∞ control of neutral stochastic delay Markovian jump systems

This paper investigates the exponential stabilisation and H_∞ control problem of neutral stochastic delay Markovian jump systems. First, a delay feedback controller is designed to stabilise the neutral stochastic delay Markovian jump system in the drift part. Second, sufficient conditions for the existence of feedback controller are proposed to ensure that the resulting closed-loop system is exponentially stable in mean square and satisfies a prescribed H_∞ performance level. Finally, numerical examples are provided to show the effectiveness of the proposed design methods.     

H∞ control and filtering of discrete-time stochastic systems with multiplicative noise

Linear discrete-time systems with stochastic uncertainties in their state-space matrices are considered. The problems of finite-horizon filtering and output-feedback control are solved, taking into account possible cross-correlations between the uncertain parameters. In both problems, a cost function is defined which is the expected value of the relevant standard H_∞ performance index with respect to the uncertain parameters. A solution to the filtering problem is obtained first by applying the adjoint system and deriving a bounded real lemma for this system. This solution guarantees a prescribed estimation level of accuracy while minimizing an upper bound on the covariance of the estimation error. The solution of the filtering problem is also extended to the infinite-horizon case. The results of the filtering problem are used to solve the corresponding output-feedback problem. A filtering example is given where a comparison is made with the results obtained using bounded uncertainty design techniques.     

Robust H∞ control for uncertain discrete stochastic time-delay systems

This paper deals with the problems of robust stabilization and robust H_∞ control for discrete stochastic systems with time-varying delays and time-varying norm-bounded parameter uncertainties. For the robust stabilization problem, attention is focused on the design of a state feedback controller which ensures robust stochastic stability of the closed-loop system for all admissible uncertainties, while for the robust H_∞ control problem, a state feedback controller is designed such that, in addition to the requirement of the robust stochastic stability, a prescribed H_∞ performance level is also required to be satisfied. A linear matrix inequality (LMI) approach is developed to solve these problems, and delay-dependent conditions for the solvability are obtained. It is shown that the desired state feedback controller can be constructed by solving certain LMIs. An example is provided to demonstrate the effectiveness of the proposed approach.     

Sampled-data-based LQ control of stochastic linear continuous-time systems

Sampled-data (SD) based linear quadratic (LQ) control problem of stochastic linear continuous-time (LCT) systems is discussed. Two types of systems are involved. One is time-invariant and the other is time-varying. In addition to stability analysis of the closed-loop systems, the index difference between SD-based LQ control and conventional LQ control is investigated. It is shown that when sample time ?T is small, so is the index difference. In addition, the upper bounds of the differences are also presented, which are O(?T2) and O(?T), respectively.     

Robust H∞ control for stochastic systems with nonlinearity,uncertainty and time-varying delay

This paper deals with the problems of robust stochastic stabilization and $H_{\infty }$ control for uncertain stochastic systems with time-varying delay and nonlinear perturbation. System uncertainties are assumed to be norm bounded and time delay is assumed to be bound and time varying with delay-derivative bounded by a constant, which may be greater than one. First, new delay-dependent criterion is proposed by exploiting delay-partitioned Lyapunov–krasovskii functional and by employing tighter integral equalities to estimate the upper bound of the stochastic differential of Lyapunov–krasovskii functional without ignoring some useful terms. Second, based on the criterion obtained, a delay-dependent criterion for the existence of a state feedback $H_{\infty }$ controller that ensures robust stochastic stability and a prescribed $H_{\infty }$ performance level of the closed-loop system for all admissible uncertainties is proposed. These developed results have advantages over some previous ones, in that they involve fewer matrix variables but have less conservatism and they also enlarge the application scope. New sufficient conditions are presented in terms of linear matrix inequality. Numerical examples are used to illustrate the effectiveness and feasibility of the proposed method.     

Discrete-time H ∞ and H 2 control with structured disturbances and probability-relaxed requirements

This paper presents a method that reduces the conservatism inherent in the disturbance representation in standard H _∞ theory. It addresses several discrete-time control synthesis problems where the disturbances acting on the system are structured by a convex family of linear filters (‘signal polytope’) and where the system disturbance attenuation level attained over the signal polytope is ensured to (just) a prescribed probability. This setup enables realistic multi-feature disturbance delimiting while accounting for uncertainty in the disturbance model itself by allowing probability waivers. The core of the probability aspects of the proposed solutions is the search for a truncated signal polytope which provides both the required probability and the best robust disturbance attenuation level. Many examples are given, including one of an aircraft output-feedback control with a polytope of low-pass filters representing different wind phenomena. The examples demonstrate that addressing realistic disturbances results in better control designs (hence better performance) and that a small certainty waiver can yield a large performance gain.     

H ∞ control for non-linear stochastic systems: the output-feedback case

The H _∞ output-feedback control problem for non-linear stochastic systems is considered. A solution for a large class of non-linear stochastic systems is introduced (including non-linear diffusion systems as a subclass). This solution is based on a bounded real lemma for non-linear stochastic systems that was previously established via a stochastic dissipativity concept. The theory yields sufficient conditions for the closed-loop system to possess a prescribed L ₂-gain bound in terms of two Hamilton Jacobi inequalities: one that is associated with the state feedback part of the problem is n-dimensional (where n is the underlying system's state dimension) and the other inequality that stems from the estimation part is 2n-dimensional. Both stationary and non–stationary systems are considered. Stability of the closed-loop system is established, both in the mean-square and the in-probability senses. As the solution to the Hamilton Jacobi inequalities may, in general, lead to a non–realisable state estimator, a modification of the associated 2n-dimensional Hamilton Jacobi inequality is made in order to circumvent this realisation problem, while preserving the system's L ₂-gain bound. For time-invariant systems, the problem of robust output-feedback is considered in the case of norm-bounded uncertainties. A solution is then derived in terms of linear state-dependent matrix inequalities.     

Optimal control of stochastic FitzHugh–Nagumo equation

This paper is concerned with the existence and uniqueness of solution for the optimal control problem governed by the stochastic FitzHugh–Nagumo equation driven by a Gaussian noise. First-order conditions of optimality are also obtained.     

H∞ and H2 control of multi-agent systems with transient performance improvement

In this paper, the H_∞ consensus control and H₂ robust control synthesised with transient performance problems are investigated for a group of autonomous agents with linear or linearised dynamics. Based on the relative information between neighbouring agents and a subset of absolute information of the agents, distributed controllers are proposed for both H_∞ and H₂ cases. Compared with the existing protocols, the one presented in this article focuses on improving the transient performance of the consensus problem. By using the tools from matrix analysis and robust control theory, conditions for the existence of controllers to those problems under an undirected communication topology are provided. Then, it is shown that the H₂ performance limit of uncertain systems under a distributed controller equals the minimum H_∞ consensus index synthesised with transient performance of a single agent by using a state feedback controller, independent of the communication topology. Finally, a simulation example as an application in Raptor-90 helicopter is proposed to illustrate the effectiveness of the theoretical results.     

Conjugation and H ∞ control of discrete-time systems

We establish conjugation notion in discrete-time systems, first introduced into the H ^∞ control theory of continuous-time systems by Kimura (1989). In discrete-time systems, conjugation is a very elementary operation on rational transfer functions that replaces some of their poles by their reflections with respect to the unit circle. With the aid of J-lossless conjugation conjugation by a J.lossless system), it is shown that the parametrization of sub-optimal solutions of H ^∞ model-matching problems is reduced to a Lyapunov-type equation. The parametrization of all solutions is given in an extremely simple way. It is further proved that the J-lossless conjugation of the H ^∞ model-matching problem is a natural state-space representation of classical interpolation in discrete-time systems.     

Mixed H2 /H∞ control under variance constraints

Dual problems, which we call output and input variance constrained H₂ /H_∞ controls, are considered. In these problems, we seek control-laws that satisfy mixed H₂ /H_∞ performance criteria, under multiple variance constraints on either outputs or inputs of time-invariant multivariable systems. The approach taken is to convert the problems into non-linear programming with both equality and inequality constraints. For both problems, the Kuhn-Tucker optimality condition is employed to obtain a first-order necessary condition for a regular point that minimizes an upper bound on the quadratic performance for the given H_∞ constraint. A second-order necessary condition and sufficiency for the strict local minimizer of the upper bound are investigated. Efficient algorithms for synthesizing the desired controllers are proposed.     

Delay-dependent H ∞ and generalized H 2 filtering for stochastic neural networks with time-varying delay and noise disturbance

This paper presents the delay-dependent \(H_\infty\) and generalized H ₂ filters design for stochastic neural networks with time-varying delay and noise disturbance. The stochastic neural networks under consideration are subject to time-varying delay in both the state and measurement equations. The aim is to design a stable full-order linear filter assuring asymptotical mean-square stability and a prescribed \(H_\infty\) or generalized H ₂ performance indexes for the filtering error systems. Delay-dependent sufficient conditions for the existence of \(H_\infty\) and generalized H ₂ filters are both proposed in terms of linear matrix inequalities. Finally, numerical example demonstrates that the proposed approaches are effective.     

Event-based H2/H∞ controllers for networked control systems

This paper is concerned with event-based H₂/H_∞ control design for networked systems with interval time-varying delays. The contributions are twofold. First, conditions for uniform ultimately bounded stability are provided in the H₂/H_∞ event-based context. The relation between the boundedness of the stability region and the threshold that triggers the events is studied. Second, a practical design procedure for event-based H₂/H_∞ control is provided. The method makes use of Lyapunov–Krasovskii functionals (LKFs) and it is characterised by its generality, as only mild assumptions are imposed on the structures of the LKF and the cost functional. The robustness and performance of the proposed technique is showed through numerical simulations.     

Robust H∞ control of uncertain stochastic Markovian jump systems with mixed time-varying delays

In this paper, robust H_∞ control for a class of uncertain stochastic Markovian jump systems (SMJSs) with interval and distributed time-varying delays is investigated. The jumping parameters are modelled as a continuous-time, finite-state Markov chain. By employing the Lyapunov-Krasovskii functional and stochastic analysis theory, some novel sufficient conditions in terms of linear matrix inequalities are derived to guarantee the mean-square asymptotic stability of the equilibrium point. Numerical simulations are given to demonstrate the effectiveness and superiority of the proposed method comparing with some existing results.