This paper addresses the stabilization problem of stochastic jump systems (SJSs) closed by a generally sampled controller. Because of the controller's switching and state both sampled, it is challenging to study its stabilization. A new stabilizing method deeply depending on the mode classifications is proposed to deal with the above sampling situation, whose quantity is equal to a Stirling number of the second kind. For the sake of finding the best stabilization effect among all the classifications, a convex optimization problem is developed, whose globally solution is proved to be existent and can be computed by an augmented Lagrangian function. More importantly, in order to further reduce the computation complexity but retaining a better performance as much as possible, a novelly improved hill-climbing algorithm is established by applying the Q-learning technique to provide an optimal attenuation coefficient. A numerical example is offered so as to verify the effectiveness and superiority of the methods proposed in this study.

翻译：本文研究了由一般采样控制器闭环的随机跳变系统的镇定问题。由于控制器的切换与状态均存在采样现象，其镇定分析具有挑战性。本文提出了一种深度依赖模态分类的新型镇定方法，其分类数量等于第二类斯特林数。为在所有分类中寻求最优镇定效果，本文建立了一个凸优化问题，并证明其全局最优解的存在性，且可通过增广拉格朗日函数进行计算。更重要的是，为在尽可能保持良好性能的同时进一步降低计算复杂度，本文通过引入Q学习技术确定最优衰减系数，建立了一种创新改进的爬山算法。数值算例验证了所提方法的有效性与优越性。