Cooperative Optimization Hunting Control of Multi-MSV Based on Reinforcement Learning - Yuanhao Wang; Weiwei Bai; Wenjun Zhang (Applied Computational Intelligence and Soft Computing)
In this paper, a cooperative hunting optimal control problem is studied for multimarine surface vehicle (MSV) systems based on reinforcement learning (RL). First, in order to enhance the efficiency of cooperative hunting, a novel task allocation method is proposed based on a leader–follower structure, where follower and leader MSVs perform the target encircling task and the target tracking task, respectively. Second, on the basis of task allocation, adaptive control design is employed for the follower MSVs to enhance the control performance of target encircling; optimal feedback control design combined with adaptive feedforward control design is considered using the RL algorithm for the leader MSVs to ensure the optimality of target tracking. Finally, the stability of the multi-MSV hunting control system is guaranteed and all signals are uniformly ultimately bounded based on the Lyapunov theory in the closed-loop system. The effectiveness of the proposed scheme is demonstrated through simulation results.