Sampled-Data Stochastic Stabilization of Markovian Jump Systems via an Optimizing Mode-Separation Method
研究通过优化模式分离方法解决马尔可夫跳变系统在采样数据控制器下的随机镇定问题，提出增强爬山算法并利用Q学习降低计算复杂度，两个例子验证了方法的有效性。
This article addresses the stochastic stabilization problem of Markovian jump systems (MJSs) closed by a sampled-data controller in the diffusion part. A novel stochastic stabilizing method is developed by optimizing the mode separations whose quantity is equal to a Stirling number of the second kind. It can be used to deal with the challenges coming from a stochastic controller's switching and state signals sampled, whose results are also less conservative compared to some existing results. In order to get the best mode separation having the best performance, an optimization problem is proposed by applying an augmented Lagrangian cost function, which can ensure the existence and calculability of a locally optimal solution. Moreover, an improved hill-climbing algorithm is established to reduce computational complexity while retaining as much performance as possible, which is enhanced by applying Q-learning technique to determine an optimal attenuation coefficient. Two examples are offered so as to verify the effectiveness and superiority of the methods given in this study.