Abstract:avoidance of underwater vehicles is an important basis for autonomous operation, but complicated constraints and imprecision of models increase the technical difficulty of path tracking in avoidance. Based on the traditional model predictive control, this paper proposes a horizontal plane collision avoidance control method, combining with many constraints of operation scene and introducing radial basis function neural network. Firstly, radial basis neural network is used to establish error compensation function to improve the accuracy of traditional dynamic model. Then, combined with the collision avoidance, the performance index function is selected in the rolling optimization stage, and the constraints such as obstacles, actuator and control stability are explicitly introduced. Finally, the simulation results show that the proposed method can control the underwater vehicle to track the path of collision avoidance in the horizontal plane.
