Model-free learning-based distributed cooperative tracking control of human-in-the-loop multi-agent systems

This article studies the model-free learning-based distributed cooperative tracking control of human-in-the-loop multi-agent systems in the presence of an active leader. The core role of human-in-the-loop is to use the ground station to send control commands to the non-zero control input of the leader, and then directly or indirectly control a group of agents to complete complex tasks. Meanwhile, three essential demands including the completely unknown system model, the control objective obtained optimally, as well as no initial admissible control strategy requirement, are satisfied simultaneously. It is worth emphasizing that the relevant results only satisfy one or two demands at most, which are essentially not applicable to this problem. In this article, a model-based human-in-the-loop learning algorithm is first presented to achieve the optimal tracking control, as well as the convergence of the proposed learning algorithm is proved. Then, a bias-based data-driven learning algorithm is proposed, which provides the potential opportunities to overcome the difficulties caused by the above-mentioned three demands. Finally, the validity of theoretical results is testified by a numerical example.