Collaborative Control of Path Following and Obstacle Avoidance for Underactuated Ships Based on Neural Network Sliding Mode
[Objective]To solve the path following and obstacle avoidance problems of underactuated ships with model uncertainty and external environmental disturbance,a neural network sliding mode adaptive control law is proposed by combining the backstepping method and radial basis function (RBF) neural network technology,and an improved artificial potential field is also proposed.[Method]Firstly,based on the error equation,the auxiliary surge velocity and yaw angle were designed.Then,sliding surfaces were designed for the control inputs separately.The RBF neural network was used to approximate the total unknown terms,and control laws and adaptive laws were designed.[Result and Conclusion]Lyapunov stability analysis proved that the closed-loop system error was ultimately uniformly bounded.Design improved artificial potential field for static and dynamic obstacles respectively to overcome the problems of local minimum and not considering the relationship between the position and relative velocity of ships and obstacles.The comparative results of simulation show that under the disturbance of waves,the convergence accuracy of the ship's path following error is higher and the obstacle avoidance is safer.The proposed control method can improve the path following and obstacle avoidance control effect.The effectiveness and robustness of the proposed algorithm have been demonstrated.
underactuated shippath followingobstacle avoidancebacksteppingradial basis function neural networksliding modeartificial potential field method
万方数据
维普
