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

被引：0
|
作者：
Guo L. [1 ]
Gao J. [1 ]
Jiao H. [2 ,3 ]
Song Y. [1 ]
Chen Y. [1 ]
Pan G. [1 ]
机构：
[1] School of Marine Science and Technology, Northwestern Polytechnical University, Xi′an
[2] Unmanned System Research Institute, Northwestern Polytechnical University, Xi′an
[3] China Ship Scientific Research Center, Wuxi
来源：
Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University | 2023年 / 41卷 / 05期
关键词：
autonomous underwater vehicle; model predictive control; path following; radial basis function neural network;
D O I：
10.1051/jnwpu/20234150871
中图分类号：
学科分类号：
摘要：
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. ©2023 Journal of Northwestern Polytechnical University.
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页码：871 / 877
页数：6
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