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, Hamilton-Jacobi-Bellman (HJB) equation constraints is presented, and a sufficient criterion on mean-square leader-following consensus of constrained-input SMASs derived. Next, novel policy iteration algorithm designed to obtain ETOC strategy. Then, an identifier-critic framework where identifier network utilized recover knowledge dynamics approximate controller via adaptive critic designs (ET-ACDs). Moreover, it proved that Zeno behavior can be excluded in sense expectation. Finally, we present two examples further verify validity scheme.