Event-Triggered Optimized Control for Nonlinear Delayed Stochastic Systems

This paper is concerned with the problem of event-triggered optimized control for uncertain nonlinear Ito-type stochastic systems with time-delay and unknown dynamic. By using fuzzy logic systems to approximate two unknown nonlinear functions with the delayed state and current state, respectively. The adaptive identifier is constructed to determine the stochastic system, and the optimized control is designed by using the identifier and adaptive dynamic programming (ADP) of actor-critic architecture. Almost all of the works are concentrated on ADP-based optimal control and it will inevitably cause the complexity of computation and requirements of persistence excitation (PE) assumption. In this paper, the ADP algorithm is obtained based on the negative gradient of a simple positive function (equivalent to the HJB equation), and so the proposed optimal control is simple and can release the PE assumption. Moreover, the event-triggered control approach is proposed to reduce computing burden and communication resources. Furthermore, we prove that the states of system and FLSs parameter errors are semi-globally uniformly ultimately bounded (SGUUB) in mean square via the adaptive identifier and the Lyapunov direct method as well as identifier-actor-critic architecture-based ADP algorithm. Finally, the effectiveness of the proposed method is illustrated through two numerical examples.

Automated summary

This paper proposes an event-triggered optimal control method for uncertain nonlinear Ito-type stochastic systems with time-delay and unknown dynamic. By using fuzzy logic systems to approximate unknown nonlinear functions and utilizing adaptive dynamic programming, the proposed method is simple and eliminates the necessity of the persistence excitation assumption. The effectiveness of the method is demonstrated through numerical examples.