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

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.