Performance-Based Human-in-the-Loop Optimal Bipartite Consensus Control for Multi-Agent Systems via Reinforcement Learning

This paper investigates the performance-based human-in-the-loop (HiTL) optimal bipartite consensus control problem for nonlinear multi-agent systems (MASs) under signed topology. First, to respond to any emergencies and guarantee the safety of MASs, the MASs are monitored by human operator sending command signals to the non-autonomous leader. Then, under the joint design architecture of prescribe-time performance function and error transformation, a novel performance index function involving transformed error and control input is developed to achieve optimal bipartite consensus with prescribed-time. Subsequently, the reinforcement learning (RL) method is utilized to learn the solution to Hamilton-Jacobian-Bellman (HJB) equation, in which the fuzzy logic systems (FLSs) are employed to implement the method. Finally, the simulation results depict the effectiveness of the constructed control scheme.