Recurrent fuzzy wavelet neural networks based on robust adaptive sliding mode control for industrial robot manipulators

被引:97
|
作者
Vu Thi Yen [1 ,2 ]
Wang Yao Nan [1 ]
Pham Van Cuong [3 ]
机构
[1] Hunan Univ, Coll Elect & Informat Engn, Changsha, Hunan, Peoples R China
[2] SaoDo Univ, Fac Elect Engn Technol, Saodo, Haiduong, Vietnam
[3] Hanoi Univ Ind, Fac Elect Engn Technol, Hanoi, Vietnam
来源
NEURAL COMPUTING & APPLICATIONS | 2019年 / 31卷 / 11期
基金
中国国家自然科学基金;
关键词
Recurrent fuzzy wavelet neural networks; Robust adaptive control; Robot manipulators; Industrial robot; TRACKING CONTROL; BACKSTEPPING CONTROL; IDENTIFICATION; SYSTEMS; DESIGN; PERFORMANCE;
D O I
10.1007/s00521-018-3520-3
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
A robust adaptive control method is proposed in this paper based on recurrent fuzzy wavelet neural networks (RFWNNs) system for industrial robot manipulators (IRMs) to improve high accuracy of the tracking control. The RFWNNs consist of four layers, and second layer has the feedback connections. Wavelet basis function is used as fuzzy membership function. In general, it is not easy to adopt a model-based method to achieve this control object due to the uncertainties of the IRM, such as unknown dynamic, disturbances and parameter variations. To solve this problem, all the parameters of the RFWNNs system are tuned online by an adaptive learning algorithm, and online adaptive control laws are determined by Lyapunov stability theorem. In addition, the robust controller is designed to deal with the approximation error, optimal parameter vectors and higher-order terms in Taylor series. Therefore, with the proposed control, the desired tracking performance, stability and robustness of the closed-loop manipulators system are guaranteed. The simulations and experimental performed on a three-link IRMs are provided in comparison with fuzzy wavelet neural network and robust neural fuzzy network to demonstrate the effectiveness and robustness of the proposed RFWNNs methodology.
引用
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页码:6945 / 6958
页数:14
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