基于?表示的时变随机Markov跳跃系统的能观性

研究时变连续和离散随机Markov跳跃系统(SMJSs)的能观性问题。基于?表示方法将时变SMJSs转化为等价的时变线性系统,根据线性系统理论得到时变连续和离散SMJSs的能观性Gramian矩阵判据。数值仿真表明了所得结论的正确性。     

线性时变广义系统的能控性与能观性问题

讨论了线性时变系统和线性时变广义系统的两个基本问题, 得到了两种判定时变系统能控性与能观性的必要条件, 该判定条件只依赖于系统矩阵A(t)和输入矩阵B(t), 不必计算系统的系统状态转移矩阵, 使得判别时变系统能控性与能观性易于实现. 说明了本文结论是线性定常系统相应结论的自然扩展. 对进一步深入研究时变系统和时变广义系统具有实际启发作用.     

时变广义系统线性二次最优控制

研究时变广义系统线性二次最优控制问题.通过引进时变广义系统脉冲能控性及脉冲能 观性等概念,建立了这类问题与标准状态空间系统二次指标问题的等价性.进而证明了解的 存在唯一性,给出了解的表示和最优反馈综合.     

一类线性时变系统的输出反馈控制

基于线性时不变系统能控能观标准型变换及非线性系统高增益观测器方法,本文研究了一类线性时变系统 的输出反馈控制问题. 通过引入时变的状态变量坐标变换,分别设计了线性时变系统的状态反馈控制器、状态观测器以及基于 状态观测器的输出反馈控制器. 进一步地,本文分别证明了观测器动态误差是渐近收敛于零的,而状态反馈控制器以及输出反馈控制器可以 保证闭环系统的渐近稳定性.     

保证能控性和能观性的采样方式

提出一种简单的采样间隔可任意选择的不等间距采样方法，它能保证对于任意能控或能观连续系统，其离散系统一定能控或能观，而与系统参数无关，以该方式离散化后的系统为周期性时变系统，因而既可直接设计时变系统的反馈控制律，也可先将一个周期内的议程化为时不变方程，再按定常系统进行控制律设计。     

基于LMI 的线性时变周期系统的稳定性及鲁棒控制

线性时变周期（LTVP）系统的能控性、能观性、稳定性、镇定性等问题的研究一般需要依赖于系统的状态转移矩阵.但是,获得一般LTVP系统的状态转移矩阵十分困难.借鉴解决线性定常系统鲁棒控制问题的思路,把周期问题转化为一类范数有界问题,避开了求取系统状态转移矩阵;设计了基于LMI的使LTVP系统镇定的无记忆反馈控制器和基于LMI的使LTVP系统鲁棒镇定无记忆反馈控制器,仿真算例验证了所提出方法的有效性.     

对线性系统状态转移矩阵的讨论

状态转移矩阵是现代控制理论的重要概念,在线性控制系统的运动分析中起着重要的作用.分别对连续时间线性时变系统、离散时间线性定常系统以及离散时间线性时变系统的状态转移矩阵进行了研究.根据常微分方程和差分方程解的唯一性,得到了判断矩阵函数是某一线性系统状态转移矩阵的充分条件,并求出了其对应的系统矩阵.实践证明了结论的正确性.     

广义系统的能控、能观性判别条件

该文讨论广义系统能控、能观性问题,给出了广义系统能控和能观性一种新的判别条件.     

基于矩阵方法的有界Petri网系统的能观性分析

Petri网和有限自动机是离散事件动态系统的两类主要研究内容.而Petri网系统的能观性分析与判别是基于Petri网的实际系统设计、优化、监测及控制的重要基础.以往关于Petri网能观测性的研究缺乏定量化的充要判别条件.本文利用代数矩阵方法研究了带有输出的有界Petri网系统的能观性问题.首先,基于矩阵的半张量积,将带有输出的有界Petri网系统的动态行为以线性方程组的形式建立了数学模型.然后,针对初始标识和当前标识,介绍了两种能观性定义.最后,基于矩阵运算建立了关于有界Petri网系统能观性的几个充分必要条件,并给出严格证明.数值算例验证了理论结果.本文提出的方法实现了有界Petri网系统能观性的矩阵运算,易于计算机实现.     

应用无源性分析研究时变非线性系统的稳定性

应用反馈系统的无源性分析研究一类时变非线性系统的稳定性,给出寻找连续衰减的 充要条件的方法,并证明对于线性系统这种方法给出的结果和Routh判据完全一致.     

Detectability and observability of discrete-time stochastic systems and their applications

This paper studies detectability and observability of discrete-time stochastic linear systems. Based on the standard notions of detectability and observability for time-varying linear systems, corresponding definitions for discrete-time stochastic systems are proposed which unify some recently reported detectability and exact observability concepts for stochastic linear systems. The notion of observability leads to the stochastic version of the well-known rank criterion for observability of deterministic linear systems. By using these two concepts, the discrete-time stochastic Lyapunov equation and Riccati equations are studied. The results not only extend some of the existing results on these two types of equation but also indicate that the notions of detectability and observability studied in this paper take analogous functions as the usual concepts of detectability and observability in deterministic linear systems. It is expected that the results presented may play important roles in many design problems in stochastic linear systems.     

Robust observability for a class of time-varying discrete-time uncertain systems

This paper introduces a concept of robust observability for a class of uncertain discrete-time systems. This notion is an extension of the standard notion of observability for discrete-time, linear time-varying systems. The uncertainty and noise are modelled deterministically via a sum quadratic constraint. A necessary and sufficient condition for robust observability is presented in terms of existence of a suitable solution to a Riccati difference equation. The set of possible initial states given noisy measurements over a finite number of sampling periods is shown to be an ellipsoid.     

How non-zero initial conditions affect the minimality of linear discrete-time systems

From the state-space approach to linear systems, promoted by Kalman, we learned that minimality is equivalent with reachability together with observability. Our past research on optimal reduced-order LQG controller synthesis revealed that if the initial conditions are non-zero, minimality is no longer equivalent with reachability together with observability. In the behavioural approach to linear systems promoted by Willems, that consider systems as exclusion laws, minimality is equivalent with observability. This article describes and explains in detail these apparently fundamental differences. Out of the discussion, the system properties weak reachability or excitability, and the dual property weak observability emerge. Weak reachability is weaker than reachability and becomes identical only if the initial conditions are empty or zero. Weak reachability together with observability is equivalent with minimality. Taking the behavioural systems point of view, minimality becomes equivalent with observability when the linear system is time invariant. This article also reveals the precise influence of a possibly stochastic initial state on the dimension of a minimal realisation. The issues raised in this article become especially apparent if linear time-varying systems (controllers) with time-varying dimensions are considered. Systems with time-varying dimensions play a major role in the realisation theory of computer algorithms. Moreover, they provide minimal realisations with smaller dimensions. Therefore, the results of this article are of practical importance for the minimal realisation of discrete-time (digital) controllers and computer algorithms with non-zero initial conditions. Theoretically, the results of this article generalise the minimality property to linear systems with time-varying dimensions and non-zero initial conditions.     

Linear systems theory revisited

This paper investigates and clarifies how different definitions of reachability, observability, controllability, reconstructability and minimality that appear in the control literature, may be equivalent or different, depending on the type of linear system. The differences are caused by (1) whether or not the linear system has state dimensions that vary with time (2) bounds on the time axis of the linear system (3) whether or not the initial state is non-zero and (4) whether or not the system is time invariant. Also (5) time-reversibility of systems plays a role. Discrete-time linear strictly proper systems are considered. A recently published result is used to argue that all the results carry over to continuous time. Out of the investigation two types of definitions emerge. One type applies naturally to systems with constant dimensions while the other applies naturally to systems with variable dimensions. This paper reveals that time-varying (state) dimensions that are allowed to be zero are necessary to obtain equivalence between minimality and (weak) reachability together with observability at the systems level. Besides their theoretical significance the results of this paper are of practical importance for model reduction and control of time-varying discrete-time linear systems because they result in minimal realizations with smaller dimensions that are also computed more easily.     

Observer-estimators for discrete-time systems

The theory of observer-estimators for linear discrete-time systems is described. Both deterministic and stochastic cases are considered; in particular, the case that some observations are noise free while others are noisy is considered. Asymptotic properties for both time-varying and time-invariant systems are analyzed and the influence of observability and detectability assumptions is considered. The results unify approaches to deterministic and stochastic state estimation problems for linear discrete-time systems. Optimal filtering in the presence of colored noise is considered as a special case.     

On the induced norms of discrete-time and hybrid time-varying systems

Characterizations for the induced norms of two types of systems are considered. It is first shown that the induced l₂ norm of a discrete-time linear time-varying system may be characterized by the existence requirement on solutions to operator algebraic Riccati equations. A similar result is derived for systems that arise in sampled-data systems involving a mixture of continuous and discrete-time signals. © 1997 by John Wiley & Sons, Ltd.     

A maximum-likelihood Kalman filter for switching discrete-time linear systems

State estimation is addressed for a class of discrete-time systems that may switch among different modes taken from a finite set. The system and measurement equations of each mode are assumed to be linear and perfectly known, but the current mode of the system is unknown. Moreover, additive, independent, normally distributed noises are assumed to affect the dynamics and the measurements. First, relying on a well-established notion of mode observability developed “ad hoc” for switching systems, an approach to system mode estimation based on a maximum-likelihood criterion is proposed. Second, such a mode estimator is embedded in a Kalman filtering framework to estimate the continuous state. Under the unique assumption of mode observability, stability properties in terms of boundedness of the mean square estimation error are proved for the resulting filter. Simulation results showing the effectiveness of the proposed filter are reported.     

On the controllability and observability of discrete-time linear time-delay systems

This article studies the controllability and observability of discrete-time linear time-delay systems, so that the two properties can play a more fundamental role in system analysis before controller and observer design is engaged. Complete definitions of controllability and observability, which imply the stabilisability and detectability, respectively, and determine the feasibility of eigenvalue assignment, are proposed for systems with delays in both state variables and input/output signals. Necessary and sufficient criteria are developed to check the controllability and observability efficiently. The proofs are based on the equivalent expanded system, but the criteria only involve the delays and matrices of the same dimension as the original system. Finally, the duality between the suggested controllability and observability is presented.     

Robust performance analysis for linear continuous/discrete time systems with linear dependent and time-varying perturbations

In this paper, both continuous and discrete-time perturbed systems subject to linear dependent and time-varying perturbations are considered. The time domain performance which related to the convergence rate of the initial states is defined based on the continuous/discrete-time Lyapunov functions. The matrix measure is used to derive the robust performance of a perturbed system.