An Event‐Triggered Data‐Driven Approach to Distributed Optimal Consensus Control of Multi‐Agent Systems

ABSTRACT This article has investigated the problem of data‐driven distributed optimal consensus control for discrete‐time multiagent systems (MASs) with unknown dynamics. A novel event‐triggered goal representation heuristic dynamic programming (GrHDP)‐based control approach has been proposed. Note that traditional adaptive dynamic programming (ADP) methods usually establish two neural networks (NNs), the critic‐actor NNs, to approximate the performance index function and control inputs, respectively. In comparison, in this work, a goal NN has been used to generate a new local internal reinforcement signal (IRS) that contains more information from the environment, and then, a three neural‐networks structure, namely the goal‐critic‐actor NNs, has been established for each agent. Furthermore, to alleviate the communication load and computation cost, the event‐triggered mechanism has been constructed to update the control input aperiodically. Using the Lyapunov stability theory, detailed stability analysis results for the closed‐loop MASs with event‐triggered controller have been shown for both cases of the triggering and non‐triggering instants. Meanwhile, the weight estimation errors of the goal‐critic‐actor NNs and the local neighborhood consensus tracking errors have been guaranteed to be all uniformly ultimately bounded (UUB). Lastly, simulation studies have shown the effectiveness and superiority of our proposed method over existing works.