Fuzzy-Based Optimal Control for Stochastic Nonlinear Systems With Constrained Inputs via Dynamic Event-Triggering

A dynamic event-based optimal learning scheme is provided in the paper for nonlinear systems subject to constrained inputs and stochastic disturbances. An actor-critic structure is constructed to learn the stochastic optimal solution, which includes critic fuzzy logic system (FLS) and actor FLS. The experience replay technique and gradient-descent adaption method are used to periodically tune the critic FLS, which can approximate the optimal cost function. Based on the static event-triggered control mechanism (ETCM), a dynamic ETCM is designed, which can incorporate past triggering information and generate a longer inter-event time. The actor FLS is updated at aperiodic jumping points, which can approximate the optimal control policy. The combination of dynamic ETCM and learning structure ensures the stochastic stability of closed-loop system. The efficiency of the controller is illustrated on a numerical example and a manipulator system.