Output event-triggered tracking synchronization of heterogeneous systems on directed digraph via model-free reinforcement learning

Abstract

In this study, an event-triggered controller for the output tracking synchronization problem in heterogeneous multi-agent systems on a directed digraph is developed. Initially, the design of the distributed observer is proposed to estimate the information of the leader. Owing to the communication among agents, there exists a coupling behavior in the design of controllers for followers. The separation theorem is exploited to realize decoupling between agents. Then, an optimal conventional controller and event-triggered controller are designed to achieve the synchronization service of the output tracking problem for agents; the latter also improves the efficiency precisely by deliberately aperiodic sampling. Additionally, we prove that the inter-event time interval is strictly positive in the event-triggered controller; that is, the Zeno behavior can be avoided. Furthermore, the optimal control gain is obtained in a model-free manner. More specifically, a model-free reinforcement learning algorithm is investigated to learn the control gain, which does not require the followers’ dynamics. Finally, two simulation examples show the availability of the algorithm.