Robust Optimal Formation Control of Heterogeneous Multi-Agent System via Reinforcement Learning

In this paper, a distributed robust optimal formation control problem is studied based on reinforcement learning for the heterogeneous multi-agent system with partial unknown system parameters. The formation system is subjected to equivalent disturbances containing parameter uncertainties and external disturbances. The proposed robust optimal controller consists of a nominal controller and a robust compensator. For the nominal controller, the reinforcement learning algorithm is proposed to obtain the optimal control input. For the robust compensator, the reinforcement learning algorithm is firstly used to identify the unknown dynamic parameters and then the robust compensator is designed to restrain the equivalent disturbances in the formation system. The robustness properties of the global multi-agent system are proven. A simulation of heterogeneous rotorcrafts is provided to verify the effectiveness of the proposed method.