China's research in ocean exploration is still in its infancy,and it is facing the challenges of complex marine environment and maritime sovereignty protection.This paper focuses on the research of intelligent underwater robot-robotic arm system UVMS.Based on the Lagrange method and Morrison equation,the dynamic model of the six-degree-of-free-dom underwater manipulator was accurately established.In order to improve the stability of the system and the accuracy of trajectory tracking,the PSO particle swarm optimization algorithm combined with RBF neural network was adopted and ap-plied to the dynamic model of the underwater manipulator.The simulation results show that compared with the traditional PID and RBF neural network algorithms,the improved PSO-RBF neural network adaptive sliding mode control algorithm can determine the control parameters about 0.3 seconds and 0.1 seconds earlier than the traditional PID and RBF neural net-work algorithms,and reach a stable state in advance.
维普
万方数据
