基于切换频度的马尔科夫网络控制系统均方指数镇定

针对一类马尔科夫网络控制系统(Networked control system, NCS),研究了其均方指数镇定问题. 首先将网络控制系统建模为离散时间切换系统,子系统间的切换过程由一个转移概率矩阵已知的马尔科夫链描述, 并给出了子系统间切换频度的范围;进而基于随机过程理论和切换系统稳定性理论, 利用状态反馈实现了网络控制系统均方指数镇定,状态反馈控制律可通过求解一组线性矩阵不等式获得. 最后通过数值仿真例子验证了本文方法的有效性.

Mean Square Exponential Stabilization of Markov Networked Control Systems Based on Switching Frequentness

This paper considers the mean square exponential stabilization for networked control systems (NCSs) based on a class of Markov models. Firstly, networked control systems are modeled as a discrete-time switched system of which the switching sequence is governed by a Markov chain with known transition probability matrix, and then a method is proposed to calculate switching frequentness. By combining the stochastic process theory as well as the switched systems stability theory, a mean square exponential stabilization method is presented. The state feedback control law can be obtained by solving a set of linear matrix inequalities. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed result.