Adaptive Neural Network Control of ROVs under Ocean Current Disturbance - Xiangheng LI; Zhaokun YAN; Jiankun LOU; Hongdong WANG (水下无人系统学报)
In view of the motion control problem of remotely operated vehicles(ROVs) under uncertain model parameters and ocean current disturbance, an adaptive back-stepping control system was designed based on the limited time command filtering and radial basis function(RBF) neural network. Firstly, a stochastic ocean current model based on the Markov process was constructed, and an ROV mathematical model under ocean current disturbance was established. Secondly, command filtering technology was introduced for the desired velocity to reduce the amount of calculation caused by the iterative derivative of the traditional back-stepping method. Thirdly, the RBF neural network was utilized to estimate the uncertainty terms and external unknown disturbances of the ROV model, and an adaptive neural network controller was designed. Finally, the Lyapunov stability theory was used to prove the stability of the closed-loop control system. The simulation results show that the controller designed in this paper can achieve precise control of ROV navigation and effectively suppress the impact of uncertainty term of the model and ocean current disturbance on ROV motion.