Dynamic event-triggered optimal control for stochastic interconnected nonlinear systems with matched disturbances via adaptive dynamic programming

Abstract

This paper presents a dynamic event-triggered adaptive dynamic programming (DETADP) algorithm to study the optimal control issue of stochastic interconnected nonlinear systems with matched disturbances. By constructing an improved cost function related to the actuator faults for each auxiliary subsystem, the original control issue is converted into finding an array of decentralized optimal control policies. In order to eliminate the effect of matching perturbations, an integral sliding mode control strategy is developed using the DETADP algorithm to ensure optimal performance for general nonlinear dynamics. When the system trajectories converge to a sliding-mode surface, equivalent sliding mode dynamics are transformed into reformulated auxiliary systems with a modified cost function. Then, a DETADP algorithm is introduced under an identifier-critic network framework, where the identifier aims to determine the stochastic dynamic, the critic aims to acquire the solutions of Hamilton–Jacobi–Bellman(HJB) equations. Furthermore, a novel dynamic event-triggering condition is designed to determine the occurrence of an event by introducing a dynamic variable. By using the Lyapunov stability theory, all signals in the closed-loop system are proved to be uniformly ultimately bounded. Finally, practical simulation examples are conducted to validate the effectiveness of the designed method.