Abstract
In this paper, a robust adaptive control method based on Dynamic Structure Fuzzy Wavelet Neural Networks (FWNNs) system is presented for trajectory tracking control of industrial robot manipulators (IRM) with uncertainties and disturbances via adaptive sliding mode control (SMC). Four layer FWNNs in the Dynamic structure FWNNs is constructed on the basis of fuzzy rules which associates with wavelet function in the consequent part, to compensate for structured and unstructured uncertainties and model complex processes. However, it is difficult to design a suitable control scheme to achieve the required approximation errors, such as friction forces, external disturbances error and parameter variations. To deal with the mentioned problems, all the parameters of the Dynamic structure FWNNs system are tuned on-line by an adaptive learning algorithm, and adaptive robust control laws are determined by Lyapunov stability theorem. By using Dynamic structure FWNNs, this control system could achieve desired tracking performance, the stability and robustness of the closed-loop manipulators system are guaranteed. In addition, the simulations and experimental performed on a three-link IRM are provided in comparison with wavelet network control (WNC) and adaptive Fuzzy control (AFC) to demonstrate the effectiveness and robustness of the proposed Dynamic structure FWNNs methodology.
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Recommended by Associate Editor Xiaojie Su under the direction of Editor Myo Taeg Lim. 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.
Vu Thi Yen received the B.S. and M.S. degrees in Automation Engineering from the Thai Nguyen University, college of engineering, Vietnam, in 2008 and 2011, respectively. Her research interests include Robot control, Fuzzy neural network, and robust control.
Wang Yao Nan received the B.S. degree in computer engineering from East China Science and Technology University (ECSTU), Fuzhou, China, in 1981, and the M.S. and Ph.D. degrees in electrical engineering from Hunan University, Changsha, China, in 1990 and 1994, respectively. He was a Post-Doctoral Research Fellow with the National University of Defense Technology, Changsha, from 1994 to 1995, a Senior Humboldt Fellow in Germany from 1998 to 2000, and a Visiting Professor with the University of Bremen, Bremen, Germany, from 2001 to 2004. He has been a Professor with Hunan University since 1995. His current research interests include robot control, intelligent control and information processing, industrial process control, and image processing.
Pham Van Cuong received the Bachelors and Master degrees from Department of Automatic Control from Military Technical Academy, Vietnam, in 2007 and 2010, respectively, and Ph.D. degrees in control Science and Engineering from Hunan University, Changsha, China, in 2015. He joined the Faculty of Electrical Engineering Technology as a Lecturer in Hanoi University of Industry, Hanoi, Viet Nam since 2003. His research interests include intelligent control theory, adaptive control, robust control, applications and robot manipulators.
Nguyen Xuan Quynh received the B.S. and M.S. degrees in Automation Engineering from best Military Technical Academy, Vietnam, in 2009 and 2011, respectively. His research interests include robot control, neural network, adaptive control and optimal control.
Vu Huu Thich received his B.S. and M.S. degrees in Automation Engineering from Hanoi University of Science and Technology, Vietnam, in 1999 and 2003, respectively. He received his Ph.D. degree in Control Engineering and Automation from Military Technical Academy, Viet Nam, in 2016. He is currently working as a Lecturer at Hanoi University of Industry. His research interests include robotic, intelligent control and neural network.
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Yen, V.T., Nan, W.Y., Van Cuong, P. et al. Robust adaptive sliding mode control for industrial robot manipulator using fuzzy wavelet neural networks. Int. J. Control Autom. Syst. 15, 2930–2941 (2017). https://doi.org/10.1007/s12555-016-0371-5
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DOI: https://doi.org/10.1007/s12555-016-0371-5