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 nonlinear 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 designed 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.