Reinforcement Learning Event-Triggered Control With Flexible Performance Assurance for Stochastic Nonlinear Systems

This article focuses on an adaptive neural optimal output feedback control strategy for stochastic nonaffine multiple input multiple output nonlinear systems with input saturation. Initially, an emerging local state estimation filter is delicately formulated to identify the unavailable states while economizing the redundant resource usage in the state estimation filter-to-controller channel. Then, the tracking error can be regulated within a flexible envelope range by designing a modified flexible global prescribed time function depending on the intermittent systems at the expense of certain user-prescribed indexes. Technically, an amended nonlinear filter featuring the hyperbolic tangent function is constructed to overcome the curse of dimensionality while compensating for the effect of neglected filter error. Meanwhile, an optimized adaptive event-triggered controller is developed to adjust the triggered threshold online to release networked resources and consumed control expenses employed in the controller-to-actuator channel. Since the trigger criteria of different channels are not correlated with each other, the designer can tune the signal delivery frequency of each channel separately following the practical requirements. The boundedness of all signals is ensured via Itô’s differential equation. Herein, two illustrative analyses verify the efficacy and feasibility of the established control algorithm.