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

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Abstract

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.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant nos. 61175075) National Hightech Research and Development Projects (Grant nos. 2012AA112312, Grant nos. 2012AA11004). The authors would like to thank the editor and the reviewers for their invaluable suggestions, which greatly improved the quality for this paper dramatically.

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Authors and Affiliations

  1. College of Electrical and Information Engineering, Hunan University, Changsha, Hunan, China

    Vu Thi Yen & Wang Yao Nan

  2. Faculty of Electrical Engineering Technology, SaoDo University, SaoDo, HaiDuong, Vietnam

    Vu Thi Yen

  3. Faculty of Electrical Engineering Technology, Hanoi University of Industry, Hanoi, Vietnam

    Pham Van Cuong

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  1. Vu Thi Yen
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  2. Wang Yao Nan
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  3. Pham Van Cuong
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Correspondence to Pham Van Cuong.

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Conflict of interest

Wang Yao Nan has received research grants from Hunan University. Vu Thi Yen is a lecturer in Saodo University, Vietnam. Pham Van Cuong is a lecturer in Hanoi University of Industry, Vietnam.

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Yen, V.T., Nan, W.Y. & Van Cuong, P. Recurrent fuzzy wavelet neural networks based on robust adaptive sliding mode control for industrial robot manipulators. Neural Comput & Applic 31, 6945–6958 (2019). https://doi.org/10.1007/s00521-018-3520-3

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  • DOI: https://doi.org/10.1007/s00521-018-3520-3

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