Research on AUV attitude control based on fuzzy RBF neural network PID
Aiming at the motion attitute control requirement of high precision and strong robustness of autonomous underwater vehicle(AUV),a motion controller of AUV based on radial basis function(RBF)neural network combined with fuzzy PID control is proposed.The RBF neural network is used to optimize the parameters of fuzzy PID controller,which effectively solves the problem that fuzzy PID control relies too much on experience and is difficult to deal with complex underwater working conditions.The simulation results show that the fuzzy RBF neural network PID controller has better response speed and anti-interference ability than the traditional fuzzy PID controller in AUV attitude adjustment,and effectively improves the AUV attitude control performance.The practical application proves that the controller can quickly converge to the desired attitude and maintain stability under complex working conditions.
autonomous underwater vehiclemotion controlradial basis function neural networkfuzzy PIDmotion controller
万方数据
维普
