Reinforcement Learning-Based Prescribed Performance Formation Control for Unmanned Surface Vehicles

This paper proposes the reinforcement learning-based prescribed performance optimal formation control approach for a fleet of unmanned surface vehicles (USVs). Firstly, to improve the prescribed performance of formation errors while ensuring collision avoidance and preserving connectivity between two successive USVs, the monotone tube boundary functions are designed. Second, to obtain realistic optimal solutions, a reinforcement learning algorithm based on the actor-critic framework is employed, with the unknown parameters approximated using a identifier neural networks. Lastly, the simulation results clearly demonstrate the viability and efficacy of the suggested control strategy inside the backstepping method.