Reinforcement Learning-Based Finite-Time Optimal Containment Control for Underactuated Surface Vehicles With Guaranteed Performance

This article presents an online reinforcement learning (RL) algorithm to learn the distributed optimal containment control solution for underactuated surface vehicles subject to modeling uncertainties and unknown ocean disturbances. First, the request of exact knowledge of vehicle system dynamics is avoided by constructing an adaptive neural network identifier. The unknown lumped disturbances are estimated by disturbance observers. Second, an actor-critic-based RL algorithm is developed to release the persistence of excitation condition. Third, based on RL algorithm and backstepping procedure, we design adaptive finite-time optimal containment controllers such that the containment errors converge into a small neighborhood around zero in finite time and satisfy the predefined performance specifications. Finally, simulation studies on underactuated surface vehicles are provided to verify the effectiveness of the presented optimal control algorithm.