基于 RBF 神经网络的自主水下航行器模型预测路径跟踪控制

A model prediction controller (MPC) based on radial basis function (RBF) neural network is designed to counter the model uncertainty and multiple constraints of the autonomous underwater vehicle (AUV). On this basis of path following control with MPC, the RBF neural network is trained online with real-time measurement data to compensate for the AUV′s model uncertainty, thus suppressing the interference of model uncertainty on the MPC and reducing its overshoot and tracking error. Simulation results show that the path following algorithm based on RBF-MPC has better transient and steady-state performance compared with the classical MPC algorithm.