离散时滞系统的无记忆稳定化控制器设计

对离散时滞系统,研究无记忆状反馈稳定化控制律的设计问题,导出了控制律存在的条件,并证明了该条件等价于一个线性矩阵不等式系统的可解性问题。通过这个线性矩阵不等式系统的可行解,给出了稳定化控制律的构造方法,进而,通过建立和求解一个凸优化问题,提出了具有较小反馈增益参数的无记忆状态反馈控制律的设计方法。     

基于线性矩阵不等式的不确定关联系统的分散鲁棒镇定

应用线性矩阵不等式（LMI）方法研究不确定性关联大系统的分散便棒镇定问题。系统中不稳定项具有数值界,可不满足匹配条件。基于不确定项的表达形式,给出了其可分散状态反馈镇定的充分条件,即一组LMIs有解。在此基础上,通过求第一凸优化问题,提出了具有较小反馈增益的分散稳定化状态反馈控制律的设计方法。仿真示例说明了该方法的有效性和优越性。     

不确定性关联时滞大系统的分散鲁棒控制——LMI方法

对一类满足匹配条件的不确定性关联时滞大系统,Lyapunov稳定性原理,给出了其可分散状态反镇定的充分条件,即一组线性矩阵不等式（LMI）有解,在此基础上,通过求解一凸优化问题,提出了具有较小的反馈增益的分散稳定化状态反馈控制律的设计方法,文中最后用示例说明了该方法的应用与优越性。     

一类不确定离散奇异系统的鲁棒稳定化

讨论了离散奇异系统矩阵E中含时不变参数不确定的鲁棒状态反馈稳定化问题.首先,在一系列等价变换下,阐述了其和一个不确定正常线性离散系统的鲁棒状态反馈稳定化问题的等价关系;然后,利用线性矩阵不等式(LMI)方法,给出了鲁棒状态反馈稳定化控制器存在的一个充分必要条件,控制器的设计方法及控制器的一个解;最后,通过一个数值算例验证了本设计方法的有效性.     

时滞相关型离散时变时滞奇异系统的鲁棒镇定

讨论含参数不确定的离散时变时滞奇异系统的时滞相关的鲁棒状态反馈稳定化问题. 在一系列等价变换下, 阐述了其和一个不确定正常线性离散时变时滞系统的鲁棒状态反馈稳定化问题的等价关系;利用矩阵不等式方法, 给出一个对所有容许的不确定, 使得闭环系统正则、因果且稳定的时滞相关鲁棒状态反馈稳定化控制器存在的充分条件以及无记忆状态反馈控制器的一个解.     

一类线性离散时滞大系统的分散镇定

用一组线性矩阵不等式给出一类线性离散时滞大系统分散能镇定的一个充分条件,进而,通过建立和求解一个凸优化问题,提出了具有较反馈增益参数的分散稳定化状态反馈控制律的设计方法,所得到的控制器不仅使得闭环境系统是稳定的,而且还可以使得闭环系统状态具有给定的衰减度。     

关联动态时滞系统的分散镇定

针对一类关联时滞系统,通过建立一个凸优化问题,提出一种具有较小反馈增益参数的分散稳定化控制器的设计方法,数值例子表明了该方法的有效性。     

关于不确定的对称组合系统的稳定化控制器与观测器的设计

本文研究了不确定的对称组合系统的稳定化控制器与观测器的设计问题，给出一种设计稳定化控制器与观测器的方法，在这种设计方法中，使给定的带有不确定性的对称组合系统稳定的状态反馈增益矩阵和观测器增益矩阵可由低阶代数Ｒｉｃｃａｔｉ方程的解导出。     

不确定系统具有圆盘区域极点约束的鲁棒控制

对一类不确定线性系统,提出了存在状态反馈控制律,使得闭环系统的所有极点均位 于一给定圆盘中的一个充分必要条件.结合控制律反馈增益参数极小化的要求,建立了一个具 有线性矩阵不等式约束的凸优化问题,通过该问题的解,可以构造一个具有较小反馈增益参数 和给定要求的控制律.所提出的方法既可应用到连续系统,也可应用到离散系统.     

关于组合系统的分散Ｈ∞控制

本文考虑线性组合系统的分散状态反馈Ｈ∞控制问题。以Ｒｉｃｃａｔｉ－ｌｉｋｅ方程半正定解的术语给出了一个使得给定的组合系统稳定并且对干扰抑制保证一个Ｈ∞界限制的分散状态反馈控制设计。对具有对称循环结构的组合系统，得到一个简单的分散控制设计程序。作为一个应用，对一族线性系统的同时Ｈ∞控制问题的可解性导出了一个充分条件。     

Stabilization of linear autonomous systems of differential equations with distributed delay

Consideration is given to the problem of optimal stabilization of differential equation systems with distributed delay. The optimal stabilizing control is formed according to the principle of feedback. The formulation of the problem in the functional space of states is used. It was shown that coefficients of the optimal stabilizing control are defined by algebraic and functional-differential Riccati equations. To find solutions to Riccati equations, the method of successive approximations is used. The problem for this control law and performance criterion is to find coefficients of a differential equation system with distributed delay, for which the chosen control is a control of optimal stabilization. A class of control laws for which the posed problem admits an analytic solution is described.     

基于观测器的动态时滞系统鲁棒控制器的设计

在状态不能由输出直接得到的情况下，通过解两个Ｒｉｃｃａｔｉ方程，对不满足匹配条件的不确定线性时滞系统进行镇定。设计了一个“有记忆型”控制器。结果表明，如果状态方程的不确定参数值限定在已知有界紧集内，则可用得到的观测器与反馈控制律对系统进行镇定。     

Memoryless stabilization of uncertain linear systems includingtime-varying state delays

The problem of stabilizing a class of uncertain time-delay systems via memoryless linear feedback is examined. The systems under consideration are linear systems with time-varying state delays. They also contain uncertain parameters (possibly time-varying) whose values are known only to within a prescribed compact bounding set. The main contribution given is to enlarge the class of time-delay systems for which one can construct a stabilizing memoryless linear feedback controller. Within this framework, a novel notion of robust memoryless stabilizability is first introduced via the method of Lyapunov functionals. Then a sufficient condition for the stabilizability is proposed. It is shown that solvability of a parameterized Riccati equation can be used to determine whether the time-delay system satisfies the sufficient condition. If there exists a positive definite symmetric solution satisfying the Riccati equation, a suitable memoryless linear feedback law can be derived     

A convergent algorithm for computing stabilizing static output feedback gains

We revisit the approach by Cao et al. that uses a fixed-structure control law to find stabilizing static output feedback gains for linear time-invariant systems. By performing singular value decomposition on the output matrix, together with similarity transformations, we present a new stabilization method. Unlike their results that involve a difficult modified Riccati equation whose solution is coupled with other two intermediate matrices that are difficult to find, we obtain Lyapunov equations. We present a convergent algorithm to solve the new design equations for the gains. We will show that our new approach, like theirs, is a dual optimal output feedback linear quadratic regulator theory. Numerical examples are given to illustrate the effectiveness of the algorithm and validate the new method.     

Robust stabilization of linear systems with norm-bounded time-varying uncertainty

In this paper, robust stabilization of a class of linear systems with norm-bounded time-varying uncertainties is considered. It is shown that for this class of uncertain systems quadratic stabilizability via linear control is equivalent to the existence of a positive definite symmetric matrix solution to a (parameter-dependent) Riccati equation. Also, a construction for the stabilizing feedback law is given in terms of the solution to the Riccati equation.     

H∞ control of nonlinear continuous-time systems based on dynamical fuzzy models

This paper presents a solution of the H_∞ control problem for a class of continuous-time nonlinear systems. The method is based on a fuzzy dynamical model of the nonlinear system. A suitable piecewise differentiate quadratic (PDQ) Lyapunov function is used to establish asymptotic stability of the closed-loop system. Furthermore, a constructive algorithm is developed to obtain the stabilizing feedback control law. The controller design algorithm involves solving a set of suitable algebraic Riccati equations. An example is given to illustrate the application of the method     

Existence of SDRE stabilizing feedback

The state-dependent Riccati equation (SDRE) approach to nonlinear system stabilization relies on representing a nonlinear system's dynamics in a manner to resemble linear dynamics, but with state-dependent coefficient matrices that can then be inserted into state-dependent Riccati equations to generate a feedback law. Although stability of the resulting closed-loop system need not be guaranteed a priori, simulation studies have shown that the method can often lead to suitable control laws. In this note, we consider the nonuniqueness of state-dependent representations. In particular, we show that if there exists any stabilizing feedback leading to a Lyapunov function with star-convex level sets, then there always exists a representation of the dynamics such that the SDRE approach is stabilizing. The main tool in the proof is a novel application of the S-procedure for quadratic forms.     

H infinity Control of nonlinear discrete-time systems based on dynamical fuzzy models

This paper presents a solution to H infinity control problem for a class of discrete-time nonlinear systems. This class of nonlinear systems can be represented by a discretetime dynamical fuzzy model. A suitable quadratic L yapunov function is used to establish asymptotic stability with an l₂-norm bound gamma of the closed-loop system. Furthermore, a constructive algorithm is developed to obtain the stabilizing feedback control law. The controller design algorithm involves solving a set of suitable algebraic Riccati equations. An example is given to illustrate the application of the method.     

A deterministic theory of estimation and control

A feedback control system can be structured for linear nonstationary process and measurement systems comprising a deterministic filter whose output is the independent variable of a linear control law. Subject to uniform controllability and observability, the filter and control gains can be specified to provide arbitrary and separable stability properties. If the filter gain is selected to produce a stabilizing effect on the state estimate, and the control gain is selected to produce a stabilizing effect on the process, the filter and control gains are shown to satisfy matrix Riccati differential equations. This suggests the use of stochastic optimal control theory when there is no quantitative measure of optimality, but it is desirable to assure the qualitative property that feedback be stabilizing. A concise derivation of the Kalman-Bucy filter is included in an appendix to illustrate the facility of approaching optimal estimation problems with the methods of stability theory.     

Cyclomonotonicity and stabilizability properties of solutions ofthe difference periodic Riccati equation

The Kalman filter associated with a discrete-time linear T-periodic system is tested. The problem considered is that of selecting an initial covariance matrix such that the periodic filter based on the first T values of the Kalman filter gain is stabilizing. Sufficient conditions are given that hinge on the cyclomonotonicity of the solution of the periodic Riccati equation. Potential applications are found in filter design, quasi-linearization techniques for the periodic Riccati equation, and the design of receding-horizon control strategies for periodic and multirate systems. When specialized to time-invariant systems, the results give rise to new sufficient conditions for the cyclomonotonicity of the solutions of the time-invariant Riccati equation and the existence of periodic stabilizing feedback