Model predictive path following control of underwater vehicle based on RBF neural network

Abstract

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.

Keywords

• 自主水下航行器
• 模型预测控制
• 径向基神经网络
• 路径跟踪控制