Event-triggered adaptive optimal tracking control for nonlinear stochastic systems with dynamic state constraints

Abstract

This paper investigates the issue of event-triggered adaptive optimal tracking control for uncertain nonlinear systems with stochastic disturbances and dynamic state constraints. To handle the dynamic state constraints, a novel unified tangent-type nonlinear mapping function is proposed. A neural networks (NNs)-based identifier is designed to cope with the stochastic disturbances. By utilizing adaptive dynamic programming (ADP) of identifier-actor-critic architecture and event triggering mechanism, the adaptive optimized event-triggered control (ETC) approach for the nonlinear stochastic system is first proposed. It is proven that the designed optimized ETC approach guarantees the robustness of the stochastic systems and the semi-globally uniformly ultimately bounded in the mean square of the NNs adaptive estimation error, and the Zeno behavior can be avoided. Simulations are offered to illustrate the effectiveness of the proposed control approach.