A path following controller for deep-sea mining vehicles considering slip control and random resistance based on improved deep deterministic policy gradient

This study aimed to develop a deep-sea mining vehicle (DSMV) path-following controller that could better reflect the actual deep-sea mining conditions. First, the dynamic model of the DSMV was improved. By introducing a nonlinear slip-control model and random environmental noise resistance, the controlled plant was developed to be closer to the actual mining operation condition. Second, an improved deep deterministic policy gradient (IDDPG) algorithm was proposed. Compared to the standard DDPG algorithm, the improved algorithm reduces the overestimation of the Q value and enhances the ability of an agent to explore the global optimum. A warm-up stage was introduced to improve stability at the beginning of training and accelerate the convergence speed of training. Third, a general reward function was designed for this type of problem. Combined with the uncertainty of the improved model, the generalization ability and adaptability to the unknown environment of the controller could be improved. Finally, through a random one-point-following training test in the simulation environment and different path-following comparison tests, the path-following control ability of the controller was verified.