跳变双线性随机系统饱和执行器的镇定

对于一类具有Markov跳变参数的双线性离散随机系统,研究其饱和执行器问题.分别采用一般二次型Lyapunov函数、饱和关联Lyapunov函数进行系统随机稳定性分析,以椭圆不变集构造随机稳定域,提出两种依赖于模态跳变率的饱和状态控制器设计方法,两种方法均以线性矩阵不等式的形式给出.     

基于扩展状态观测器的随机隐Markov正跳变系统有限时间异步控制

针对一类含不匹配扰动的随机隐Markov跳变系统, 本文研究了基于扩展状态观测器(ESO)的有限时间异步 控制问题. 首先, 引入一组扩展变量将隐Markov跳变系统转换成一组新的随机扩展系统, 补偿不匹配扰动对系统控 制输出的影响. 基于Lyapunov–Krasovskii泛函方法, 给出使得基于ESO的闭环随机隐Markov增广跳变系统是正系 统, 且有限时间有界的充分条件. 进而得到直接求解观测器增益和控制器增益的线性矩阵不等式. 最后, 通过仿真结 果验证了本文所设计的异步状态反馈控制器和观测器的有效性和可行性.     

一类不确定多输入模糊双线性系统的鲁棒H∞控制

针对一类带有参数不确定性和干扰的多输入模糊双线性系统(FBS)的鲁棒H_∞控制问题,使用并行分布补偿算法(PDC)设计了模糊控制器,得到了整个模糊控制系统鲁棒全局稳定的充分条件,控制器的设计可以通过求解一系列线性矩阵不等式(LMI)获得.仿真例子验证了方法的有效性.     

基于Takagi-Sugeno模糊双线性模型的连续搅拌反应釜H∞控制

本文研究了一类连续搅拌反应釜(CSTR)系统的H1控制问题. 系统中的非线性动态特性可采用Takagi-Sugeno(T-S)模糊双线性模型进行描述. 通过引入两个自由矩阵, 给出一个新的保证闭环模糊双线性系统在H1性能指标下全局渐近稳定的充分条件和控制器设计方法, 并且该条件最终可归结为求解一组线性矩阵不等式的可行性问题. CSTR系统的仿真结果表明设计方法的有效性.     

跳变双线性随机离散组合系统的保成本分散控制

研究一类跳变双线性随机离散组合系统的保成本分散控制问题．首先给出问题可解的充分条件,然后基于线性矩阵不等式方法设计保成本分散状态反馈控制律．理想的保成本分散状态反馈控制器可通过应用现有的软件,求解一组线性矩阵不等式而得到．仿真例子说明了该方法的有效性.     

欺骗攻击环境下具有执行器故障的跳变耦合 信息物理系统的同步控制

针对一类离散Markov跳变耦合信息物理系统(CPS)的同步控制问题,在考虑系统参数跳变、耦合参数跳变、控制信息不完全和人为攻击的情况下,设计同步控制器实现CPS的同步.首先,给出具有随机欺骗攻击和执行器故障的Markov跳变耦合CPS模型.其次,基于矩阵Kronecker积,得到同步误差系统,将CPS的同步控制问题转化为同步误差系统的稳定性分析问题.再次,通过构造合适的Lyapunov-Krasovskii泛函,并利用Lyapunov稳定性理论和线性矩阵不等式方法得到使同步误差系统稳定的充分条件,在此基础上,设计同步控制器实现对Markov跳变耦合CPS的同步控制.最后,通过数值仿真例子说明该同步控制器设计方法的有效性.     

基于观测器的非线性系统H_∞模糊可靠控制

研究了基于观测器的非线性系统H∞模糊可靠控制问题.采用T-S模糊模型对非线性系统进行建模,用模糊观测器重构系统状态.在系统发生故障时满足给定H∞性能的约束下.最小化正常情况下的H∞性能,实现次优H∞模糊可靠控制.提出了两种应用线性矩阵不等式(LMI)的H∞模糊可靠控制器设计方法.分别采用两步法和相似变换法将双线性矩阵不等式问题转化为LMI问题.仿真示例验证了所提出方法的有效性.     

广义马氏跳变系统在一般转移速率下的控制器设计

针对转移概率不能精确获得并且部分未知的广义马氏跳变系统, 分别讨论在模态依赖控制器和模态独立控制器条件下的系统镇定问题. 与现有结果相比, 所提研究方法具有较小的保守性, 能够有效地解决实际问题. 首先, 运用自由权矩阵方法, 得到了广义马氏跳变系统在转移速率满足上述一般条件时系统随机容许的充分条件. 在此基础上, 以线性矩阵不等式(Linear matrix inequality, LMI)的形式分别给出了模态依赖和模态独立控制器的求解条件. 最后, 通过数值算例验证设计方法的有效性和优越性.     

模糊奇异摄动系统及其稳定性分析与综合

通过扩展常规Takagi-Sugeno模糊系统,定义了一类模糊奇异摄动系统,利用矩阵不等 式表达出了在摄动参数足够小时的闭环稳定性.镇定并行分布式补偿控制器增益和共同的Lyapunov 函数可利用两步法得到,并可分别归结于一组线性矩阵不等式和双线性矩阵不等式,后者 可以利用迭代线性矩阵不等式方法有效地求解.文末给出了数值和仿真实例.     

乘性Markov 跳变系统鲁棒方差控制

针对一类具有乘性噪声和参数不确定性的Markov跳变参数系统,研究使得闭环系统的稳态状态方差小于某个给定的上界,同时满足一定H∞性能的状态反馈鲁棒方差控制器设计问题.运用线性矩阵不等式(LMI)方法,对系统进行了方差分析,给出并证明了控制器存在的条件,进而用一组线性矩阵不等式的可行解给出了控制器的一个参数化表示.最后的仿真结果验证了该方法的有效性.     

Robust fuzzy control for uncertain discrete-time nonlinear Markovian jump systems without mode observations

This paper studies the robust fuzzy control problem of uncertain discrete-time nonlinear Markovian jump systems without mode observations. The Takagi and Sugeno (T-S) fuzzy model is employed to represent a discrete-time nonlinear system with norm-bounded parameter uncertainties and Markovian jump parameters. As a result, an uncertain Markovian jump fuzzy system (MJFS) is obtained. A stochastic fuzzy Lyapunov function (FLF) is employed to analyze the robust stability of the uncertain MJFS, which not only is dependent on the operation modes of the system, but also directly includes the membership functions. Then, based on this stochastic FLF and a non-parallel distributed compensation (non-PDC) scheme, a mode-independent state-feedback control design is developed to guarantee that the closed-loop MJFS is stochastically stable for all admissible parameter uncertainties. The proposed sufficient conditions for the robust stability and mode-independent robust stabilization are formulated as a set of coupled linear matrix inequalities (LMIs), which can be solved efficiently by using existing LMI optimization techniques. Finally, it is also demonstrated, via a simulation example, that the proposed design method is effective.     

Mode-independent robust stabilization for uncertain Markovian jump nonlinear systems via fuzzy control.

This paper is concerned with the robust-stabilization problem of uncertain Markovian jump nonlinear systems (MJNSs) without mode observations via a fuzzy-control approach. The Takagi and Sugeno (T-S) fuzzy model is employed to represent a nonlinear system with norm-bounded parameter uncertainties and Markovian jump parameters. The aim is to design a mode-independent fuzzy controller such that the closed-loop Markovian jump fuzzy system (MJFS) is robustly stochastically stable. Based on a stochastic Lyapunov function, a robust-stabilization condition using a mode-independent fuzzy controller is derived for the uncertain MJFS in terms of linear matrix inequalities (LMIs). A new improved LMI formulation is used to alleviate the interrelation between the stochastic Lyapunov matrix and the system matrices containing controller variables in the derivation process. Finally, a simulation example is presented to illustrate the effectiveness of the proposed design method.     

Robust H-infinity fuzzy control for uncertainMarkovian jump nonlinear singular systems withwiener process

This paper deals with the problems of robust stochastic stabilization and H-infinity control for Markovian jump nonlinear singular systems with Wiener process via a fuzzy-control approach. The Takagi-Sugeno (T-S) fuzzy model is employed to represent a nonlinear singular system. The purpose of the robust stochastic stabilization problem is to design a state feedback fuzzy controller such that the closed-loop fuzzy system is robustly stochastically stable for all admissible uncertainties. In the robust H-infinity control problem, in addition to the stochastic stability requirement, a prescribed performance is required to be achieved. Linear matrix inequality (LMI) sufficient conditions are developed to solve these problems, respectively. The expressions of desired state feedback fuzzy controllers are given. Finally, a numerical simulation is given to illustrate the effectiveness of the proposed method.     

Delay-range-dependent robust stabilisation and H ∞ control for nonlinear uncertain stochastic fuzzy systems with mode-dependent time delays and Markovian jump parameters

This article focuses on the problems of robust stabilisation and H _∞ control for nonlinear uncertain stochastic systems with mode-dependent time delay and Markovian jump parameters represented by the Takagi–Sugeno (T-S) fuzzy model approach. The system under consideration involves parameter uncertainties, Itô-type stochastic disturbances, Markovian jump parameters and unknown nonlinear disturbances. The purpose is to design a state feedback controller such that the closed-loop system is robustly exponentially stable in the mean square and satisfies a prescribed H _∞ performance level. Novel delay-range-dependent conditions in the form of linear matrix inequalities (LMIs) are derived for the solvability of robust stabilisation and H _∞ control problem. A desired fuzzy controller can be constructed by solving a set solutions of LMIs and can be easily calculated by Matlab LMI control toolbox. Finally, a numerical example is presented to illustrate the proposed method.     

On stabilization of bilinear uncertain time-delay stochasticsystems with Markovian jumping parameters

In this paper, we investigate the stochastic stabilization problem for a class of bilinear continuous time-delay uncertain systems with Markovian jumping parameters. Specifically, the stochastic bilinear jump system under study involves unknown state time-delay, parameter uncertainties, and unknown nonlinear deterministic disturbances. The jumping parameters considered here form a continuous-time discrete-state homogeneous Markov process. The whole system may be regarded as a stochastic bilinear hybrid system that includes both time-evolving and event-driven mechanisms. Our attention is focused on the design of a robust state-feedback controller such that, for all admissible uncertainties as well as nonlinear disturbances, the closed-loop system is stochastically exponentially stable in the mean square, independent of the time delay. Sufficient conditions are established to guarantee the existence of desired robust controllers, which are given in terms of the solutions to a set of either linear matrix inequalities (LMIs), or coupled quadratic matrix inequalities. The developed theory is illustrated by numerical simulation     

H∞ fuzzy control design of discrete‐time nonlinear active fault‐tolerant control systems

This paper is concerned with the problem of H_∞ fuzzy controller synthesis for a class of discrete‐time nonlinear active fault‐tolerant control systems (AFTCSs) in a stochastic setting. The Takagi and Sugeno (T–S) fuzzy model is employed to exactly represent a nonlinear AFTCS. For this AFTCS, two random processes with Markovian transition characteristics are introduced to model the failure process of system components and the fault detection and isolation (FDI) decision process used to reconfigure the control law, respectively. The random behavior of the FDI process is conditioned on the state of the failure process. A non‐parallel distributed compensation (non‐PDC) scheme is adopted for the design of the fault‐tolerant control laws. The resulting closed‐loop fuzzy system is the one with two Markovian jump parameters. Based on a stochastic fuzzy Lyapunov function (FLF), sufficient conditions for the stochastic stability and H_∞ disturbance attenuation of the closed‐loop fuzzy system are first derived. A linear matrix inequality (LMI) approach to the fuzzy control design is then developed. Moreover, a suboptimal fault‐tolerant H_∞ fuzzy controller is given in the sense of minimizing the level of disturbance attenuation. Finally, a simulation example is presented to illustrate the effectiveness of the proposed design method. Copyright © 2008 John Wiley & Sons, Ltd.     

Dissipative control for Markov jump non-linear stochastic systems based on T–S fuzzy model

The dissipative analysis and control problems for a class of Markov jump non-linear stochastic systems (MJNSSs) are investigated. A sufficient condition for the dissipativity of MJNSSs is given in terms of coupled non-linear Hamilton–Jacobi inequalities (HJIs). Generally, it is difficult to solve the coupled HJIs. In this paper, based on T–S fuzzy model, the dissipative analysis and controller design for MJNSSs is proposed via solving a set of linear matrix inequalities (LMIs) instead of HJIs. Finally, a numerical example is presented to show the effectiveness of the proposed method.