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

• Published in: Neurocomputing (Amsterdam) Vol. 412; pp. 63 - 71
• Main Authors: Liu, Hao, Meng, Qingyao, Peng, Fachun, Lewis, Frank L.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 28.10.2020
• Subjects: Formation control; Heterogeneous systems; Multi-agent systems; Reinforcement learning; Unmanned aerial vehicles; Heterogeneous systems; Formation control; Unmanned aerial vehicles; Reinforcement learning; Multi-agent systems
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2020.06.040

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.