Control Design of a Marine Vessel System Using Reinforcement Learning

In this paper, our main goal is to solve optimal control problem by using reinforcement learning (RL) algorithm for marine surface vessel system with known dynamic. And this algorithm is an optimal control algorithm based on policy iteration (PI), and it can obtain the suitable approximations of cost function and the optimized control policy. There are two neural networks (NNs), where critic NN aims to estimate the cost-to-go and actor NN is utilized to design suitable input controller and minimize the tracking error. A novel tuning method is given for critic NN and actor NN. The stability and convergence are proven by Lyapunov's direct method. Finally, the numerical simulations are conducted to demonstrate the feasibility and superiority of presented algorithm.