非线性扰动不确定系统的可靠保成本控制

Nan Wang, Nan Xie
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引用次数: 2

摘要

网络控制系统(NCS)是随着网络技术的不断发展而发展起来的分布式实时反馈控制系统。它一般由网络、控制器、执行器和传感器组成。它给许多领域的人们带来了极大的方便,但也存在着许多问题,这使得控制系统的研究更加复杂。近年来,人们在解决可靠保成本控制器的设计问题方面做了一些努力,并在连续时间和离散时间的保成本控制器设计方面取得了一些良好的效果。然而,对于具有时变状态延迟和执行器失效的非线性不确定系统的可靠保成本控制器设计,文献研究很少。研究具有时变状态延迟和非线性扰动的不确定系统对给定二次代价函数的可靠保代价控制问题。问题是设计一个可靠的保证代价状态反馈控制律,使闭环系统渐近稳定,闭环代价函数值不大于给定的上界。首先,利用线性矩阵不等式(LMI)构造Lyapunov稳定性函数,给出了可靠保成本控制律的存在条件;其次,通过求解具有LMI约束的凸优化问题,给出了最优可靠保成本控制器的设计方法,使闭环系统的保成本上界最小;最后,数值仿真结果验证了所提方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reliable Guaranteed Cost Control of Uncertain Systems with Nonlinear Perturbations
Network control system (NCS) is a distributed real-time feedback control system with the continuous development of network technology. It is generally composed of network, controller, actuator and sensor. It has brought great convenience to people in many fields, but it also has many problems, which makes the research of control system more complicated. Recently, there have been some efforts to tackle the reliable guaranteed cost controller design problem, and some good results have also been obtained for the continuous-time and for the discrete-time. However, there have been few results in the literature of an investigation for the reliable guaranteed cost controller design of nonlinear uncertain systems with time-varying state delay and actuator failure. This paper concerns the reliable guaranteed cost control problem of uncertain systems with time-varying state delay and nonlinear perturbations for a given quadratic cost function. The problem is to design a reliable guaranteed cost state feedback control law which can tolerate actuator failures, such that the closed-loop system is asymptotically stable and the closed-loop cost function value is not more than a specified upper bound. Firstly, the existence condition of reliable guaranteed cost control law is given by constructing Lyapunov stability function and using linear matrix inequality (LMI). Secondly, the design method of the optimal reliable guaranteed cost controller is given by solving the convex optimization problem with LMI constraints, which minimizes the upper bound of guaranteed cost for closed-loop systems. In the end, the numerical simulation result illustrates the effectiveness of the proposed method.
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