Thuy Van Tran. Int. Journal of Engineering Research and Application www.ijera.com ISSN : 2248-9622, Vol. 6, Issue 3, ( Part -4) March 2016, pp.131-137 www.ijera.com 131 | Page Adaptive Fuzzy-Neural Control Utilizing Sliding Mode Based Learning Algorithm for Robot Manipulator Thuy Van Tran*, YaoNan Wang** * (College of Electrical and Information Engineering, Hunan University, China; Faculty of Electrical Engineering, Hanoi University of Industry, Vietnam; Email: tranthuyvan.haui@gmail.com) ** (College of Electrical and Information Engineering, Hunan University, China; Email: yaonan@hnu.edu.cn) ABSTRACT This paper introduces an adaptive fuzzy-neural control (AFNC) utilizing sliding mode-based learning algorithm (SMBLA) for robot manipulator to track the desired trajectory. A traditional sliding mode controller is applied to ensure the asymptotic stability of the system, and the fuzzy rule-based wavelet neural networks (FWNNs) are employed as the feedback controllers. Additionally, a novel adaptation of the FWNNs parameters is derived from the SMBLA in the Lyapunov stability theorem. Hence, the AFNC approximates parameter variation, unmodeled dynamics, and unknown disturbances without the detailed knowledge of robot manipulator, while resulting in an improved tracking performance. Lastly, in order to validate the effectiveness of the proposed approach, the comparative simulation results of two-degrees of freedom robot manipulator are presented. Keywords – traditional sliding mode control (TSMC), adaptive fuzzy neural control (AFNC), fuzzy rule-based wavelet neural network (FWNN), sliding mode-based learning algorithm (SMBLA), degrees of freedom robot manipulator (DOFRM) I. INTRODUCTION Generally, various uncertainties comprising parameter variation, unmodeled dynamics, and unknown disturbances influence the tracking performances of robot manipulator [1, 2]. In the designing of reference model based control system, it is difficult for determining a mathematical model correctly. Because the traditional controllers (i.e., robust controller [3], sliding mode controller [4]) are time-invariant controllers, this term causes nonlinearities and discontinuities which renders traditional control invalid. So the requirement of the intelligent control approaches (ICAs) is that reducing the impact of the various uncertainties in the design process. During the last decades, the ICAs (i.e., neural network control (NNC) [5], and fuzzy logic control (FLC) [6]) have been largely applied for controlling the motion of robot manipulators [7, 8]. The topical trend of researches is that integrating the traditional control methods with the ICAs for the improvement in the performance of system [9-11]. Besides, based on the combination of the rule reasoning of fuzzy systems and the learning capability of neural networks without the prior knowledge, the fuzzy-neural network control (FNNC) methods are also designed to provide higher robustness than both NNC and FLC [12-14]. In the training of artificial neural networks (ANNs) and fuzzy-neural networks (FNNs), different learning algorithms containing gradient descent-based algorithm (GDBA) [15] and evolutionary computation-based algorithm (ECBA) [16, 17] have been utilized. However, the convergence rate of GDBA is sluggish due to the involvement of partial derivatives, specifically when the solution space is complicated. For the ECBA, the stability and optimal values are difficultly reached by using stochastic operators, and the high calculation is still a burden. It is well-known that sliding mode control (SMC) is a method which can ensure the stability and robustness in both the case of uncertainties and computationally intelligent systems [18]. By using the SMC strategy in the online learning for ANNs and FNNs, sliding mode- based learning algorithm (SMBLA) can guarantee better convergence and more robust than conventional learning approaches [19, 20]. It is different from GDBA in feedback-error learning [21], the network parameters are updated by SMBLA in the way that the learning error is enforcedly satisfied a stable equation. In this paper, an adaptive fuzzy-neural control (AFNC) using SMBLA is proposed for tracking desired trajectory of robot manipulator. In the proposed control method, the traditional sliding mode controller (TSMC) is applied for guaranteeing the asymptotic stability of the control system, and the fuzzy rule-based wavelet neural networks RESEARCH ARTICLE OPEN ACCESS