Observer-based event-triggered optimal control for unknown nonlinear stochastic multi-agent systems with input constraints

This paper addresses the observer-based event-triggered optimal control (ETOC) for unknown non-linear Ito-type stochastic multi-agent systems (SMASs) with input constraints. To begin with, the event -triggered stochastic Hamilton-Jacobi-Bellman (HJB) equation with input constraints is presented, and a sufficient criterion on optimal mean-square leader-following consensus of constrained-input SMASs is derived. Next, a novel event-triggered policy iteration algorithm of constrained-input SMASs is de-signed to obtain the ETOC strategy. Then, an identifier-critic framework is designed where the observer -based identifier network is utilized to recover the knowledge of unknown stochastic dynamics and the constrained-input approximate event-triggered optimal controller is designed via event-triggered adaptive critic designs (ET-ACDs). Moreover, it is proved that the Zeno behavior can be excluded in the sense of expectation. Finally, we present two examples to further verify the validity of the ETOC scheme. (c) 2023 The Franklin Institute. Published by Elsevier Inc. All rights reserved.