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Sampled-Data State-Feedback Stabilization of Probabilistic Boolean Control Networks: A Control Lyapunov Function Approach

机译:概率布尔控制网络的采样数据状态反馈稳定:控制Lyapunov功能方法

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This article investigates the partial stabilization problem of probabilistic Boolean control networks (PBCNs) under sample-data state-feedback control (SDSFC) with a control Lyapunov function (CLF) approach. First, the probability structure matrix of the considered PBCN is represented by a Boolean matrix, based on which, a new algebraic form of the system is obtained. Second, we convert the partial stabilization problem of PBCNs into the global set stabilization one. Third, we define CLF and its structural matrix under SDSFC. It is found that the existence of a CLF is equivalent to that of SDSFC. Then, a necessary and sufficient condition is obtained for the existence of CLF under SDSFC, based on which, all possible sample-data state-feedback controllers and corresponding structural matrices of CLF are designed by two different methods. Finally, examples are given to illustrate the efficiency of the obtained results.
机译:本文调查了采样数据状态反馈控制(SDSFC)下的概率布尔控制网络(PBCNS)的部分稳定问题,控制Lyapunov函数(CLF)方法。首先,所考虑的PBCN的概率结构矩阵由布尔矩阵表示,基于以下,获得系统的新代数形式。其次,我们将PBCNS的部分稳定问题转换为全球设定稳定化。第三,我们在SDSFC下定义CLF及其结构矩阵。发现CLF的存在等同于SDSFC的存在。然后,基于其中,基于以下,基于以下,通过两种不同的方法设计了所有可能的样本数据状态反馈控制器和CLF的相应结构矩阵的必要和充分的条件。最后,给出了实施例来说明所得结果的效率。

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