Action-dependent heuristic dynamic programming for hybrid-order systems based on dynamic event-triggered mechanism

We investigate the distributed optimal consensus control problem based on action-dependent heuristic dynamic programming (ADHDP). In order to strike a stable balance between the learning cost of reinforcement learning and the resource utilization efficiency of the hybrid-order multi-agent systems (MASs), we propose an improved dynamic event-triggered ADHDP (dET-ADHDP) method. This approach can non-periodically explore the control policy distribution using the online action-dependent actor-critic (ADAC) learning framework. Meanwhile, it can dynamically adjust the trigger lower bound by exploiting the designed trigger threshold function, and adaptively decide the signal trigger moment during the ADAC learning process. In addition, we demonstrate the boundedness of the ADAC network weights and show that under the designed dynamic event-triggering rules, the MASs can asymptotically achieve optimal tracking control without Zeno phenomenon. Finally, compared with the traditional static counterparts, simulation experiments demonstrate that the proposed dynamic eventtriggered ADAC (dET-ADAC) algorithm has more efficient resource utilization while maintaining satisfactory learning performance.