Event-Based Optimal Containment Control for Constrained Multiagent Systems Using Integral Reinforcement Learning

An optimal event-driven containment control problem is studied for partially unknown nonlinear multiagent systems with input constraints and state constraints. Its novelty lies in the optimization of the performance index while ensuring constraints handling abilities on states and inputs. First, an improved discounted cost function is constructed, and the state and input constraint information are encoded into the cost function by barrier functions and nonquadratic utility functions, respectively. Then, the approximate distributed optimal containment control policy is derived by an integral reinforcement learning (IRL)-based adaptive critic design, where the IRL technique can overcome the limitation of known drift dynamics in previous results. In critic neural networks learning, the weight tuning law is presented by virtue of the concurrent learning technique, which relaxes the persistence of excitation conditions by storing appropriate historical data. In order to reduce the amount of information transmitted through the controller-to-actuator channel, a containment error-dependent dynamic event-triggered mechanism is defined. Theoretical results indicate that signals in closed-loop systems driven by event-triggered optimal controllers are uniformly ultimately bounded, and Zeno behavior is avoided. Finally, the effectiveness of the developed method is illustrated by a simulation example on multiple single-link robot manipulators.