A Data-Based Adaptive Consensus Control Algorithm of Discrete-Time Multi-Agent Systems

This paper addresses the consensus tracking problem of leader-follower discrete-time multi-agent systems. To overcome the shortcomings of existing reinforcement learning (RL) algorithms, we propose a novel data-based adaptive reinforcement learning algorithm by introducing adaptive parameter. Distinguishing itself from most existing work, the study assumes that the dynamics of all agents are entirely unknown and the complete state vector is not accessible, the algorithm proposed only requires some input-output data relevant to the agents to be available. Based on this representation, the algorithm strikes a balance between data-based value iteration and data-based policy iteration, obviates the need for the limitation of initial control, exhibits a fast rate of convergence, and achieves the consensus in multi-agent systems. Through comprehensive simulation research, the effectiveness of the proposed algorithm is verified.