Heterogeneous formation control of multiple UAVs with limited-input leader via reinforcement learning

In this brief, a distributed optimal control method via reinforcement learning is proposed to address the heterogeneous unmanned aerial vehicle (UAV) formation trajectory tracking problem. The UAV formation is composed of a virtual leader with limited nonzero input and several follower vehicles with different unknown dynamics. The proposed control law contains a distributed observer and a model-free off-policy reinforcement learning (RL) protocol. The distributed optimal trajectory tracking problem is formulated for the heterogeneous formation system. A RL algorithm is designed to obtain the optimal control input online without any knowledge of the followers’ dynamics. Simulation example illustrates the effectiveness of the proposed method.