Optimal event-triggered control for multi-agent systems with hierarchical framework

This study investigates the event-triggered optimal control problem for a class of linear second-order multi-agent systems (MASs) with external disturbances. A hierarchical framework is proposed to address the challenges that arise from the information of the coupled neighbors and external disturbances, integrating the communication, learning, and control layers. Specifically, the communication layer utilizes event-triggered mechanisms (ETMs) to transmit neighbor information, facilitating virtual consensus. The learning layer connects the communication and control layers, employing reinforcement learning (RL) to optimize tracking control with ETMs. The control layer achieves real consensus by aligning the agent states with the processed information from the communication layer. Moreover, this framework effectively mitigates the effects of coupled neighbor information on the controller and suppresses the transmission of external disturbances through the communication network. Finally, two simulation examples are used to verify the anti-interference of the hierarchical framework i.e., it’s still possible to achieve consensus after being disturbed and the effectiveness of considering the reinforcement learning layer via event-triggered mechanism which reduces the communication and learning burden to achieve optimal control.