Adaptive RBF Neural Network Based Backsteppting Control for Supercavitating Vehicles

被引：0
|
作者：
Li, Yang [1 ]
Liu, Ming-Yong [1 ]
Zhang, Xiao-Jian [1 ]
机构：
[1] School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an,710072, China
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2020年 / 46卷 / 04期
基金：
中国国家自然科学基金;
关键词：
Control theory - Uncertainty analysis - Backstepping - Radial basis function networks - Vehicles - Functions - Adaptive control systems - Attitude control;
D O I：
10.16383/j.aas.2018.c170387
中图分类号：
学科分类号：
摘要：
This paper is proposed for the problems of model uncertainty such as the control of supercavitating vehicles. Firstly, the nominal model of supercavitating vehicles is built based on the analysis of the vehicle dynamic characteristics. Then we rewrite it as the uncertainty feedback system, and an orbit and attitude controller is designed via the backstepping control theory. The radial basis function (RBF) neural networks are presented to approximate and compensate the unknown functions, otherwise, the weights of the neural networks are designed by the adaptive method based on the Lyapunov theory, and the stability proof is also proposed. Finally, the simulations prove the effectiveness of the above controllers. Copyright © 2020 Acta Automatica Sinica. All rights reserved.
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页码：734 / 743
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