Human-in-the-loop Formation-containment Safe Control for Multi-agent Systems via Reinforcement Learning

This paper designs the optimal formation containment safe tracking control protocol for nonlinear second-order multi-agent systems (MASs) with unknown dynamics. A human operator can interact with the tracking leader of MASs to improve security and communicate with other agent through the MASs network in an obstacle-laden environment. Primarily, adaptive neural networks are utilized to identify the unknown nonlinear dynamics parameter and ensure the identification state errors converge asymptotically. Additionally, the optimized safe control design incorporates an actor-critic architecture based on reinforcement learning (RL) to approximate the Hamilton-Jacobi-Bellman (HJB) equation for nonlinear MASs. This distributed security control of MASs achieves optimal tracking using only local connectivity information. Finally, the proposed optimal algorithm for MASs safe formation-containment control is verified with multiple unmanned aerial vehicle (UAV) systems in an unknown obstacle environment.