Model‐based periodic event‐triggered stabilization for continuous‐time stochastic nonlinear systems

Abstract

AbstractIn this paper, we investigate a model‐based periodic event‐triggered control framework for continuous‐time stochastic nonlinear systems. In this framework, an auxiliary approximate discrete‐time model of stochastic nonlinear systems is constructed in the controller module, which is utilized not only to design a discrete‐time controller but also as a state predictor within trigger intervals. This discrete controller design approach, the strategy of state prediction, and the periodic detection strategy for the trigger rule not only provide a manner of more direct and easier implementation on the digital platform but also effectively reduce the communication load while a satisfactory control performance is maintained. Additionally, the mean‐square exponentially stabilization for continuous‐time stochastic nonlinear systems is achieved, in which a guideline for determining the maximum admissible sampling period is provided and the periodic event trigger rule is designed. The final numerical simulation also supports the effectiveness of our proposed framework.