Neuro-Optimal Event-Triggered Impulsive Control for Stochastic Systems via ADP

Abstract

This article presents a novel neural-network-based optimal event-triggered impulsive control method. First, a novel general-event-based impulsive transition matrix (GITM) is constructed to represent the probability distribution evolving characteristics regarding all system states across the impulsive actions, rather than the prefixed timing sequence. On the foundation of this GITM, the event-triggered impulsive adaptive dynamic programming (ETIADP) algorithm and its high-efficiency version (HEIADP) are developed to deal with the optimization problems for stochastic systems with event-triggered impulsive controls. It is shown that the obtained controller design scheme can reduce the computational and communication burden caused by updating the controller periodically. By analyzing the admissibility, monotonicity, and optimality properties of ETIADP and HEIADP, we further establish the approximation error bound of the neural networks to address the connection between the ideal and neural-network-based realizations of the present methods. It is proven that the iterative value functions of both the ETIADP and HEIADP algorithms fall in a small neighborhood of the optimum as the iteration index increases to infinity. By adopting a novel task synchronization mechanism, the proposed HEIADP algorithm fully utilizes the computing resources of multiprocessor systems (MPSs), while significantly reducing the memory requirement compared to traditional ADP approaches. Finally, we carry out a numerical study to show that the proposed methods can fulfill the desired goals.

Event-Triggered Delayed Impulsive Control for Stochastic Delayed Systems

“…The system only executes control tasks when system's situation reaches or violates the established triggering conditions. In recent years, eventtriggered impulsive control have be proposed and investigated by combining eventtriggered control with impulsive control to reduce the sampling frequency and communication frequency [13][14][15][16][17][18]. In fact, not only will there be time delays within the system, but time delays are also inevitable when sampling and transmitting impulse information.…”