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Robust Adaptive Sliding Mode control for Industrial Robot Manipulator using Fuzzy Wavelet Neural Networks

Vu Thi Yen, Wang Yao Nan, Pham Van Cuong*, Nguyen Xuan Quynh, Vu Huu Thich
International Journal of Control, Automation, and Systems, vol. 15, no. 6, pp.2930-2941, 2017

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."

Keyword : Fuzzy wavelet neural networks, industrial robot, robust adaptive control, robot manipulators.

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