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Mathematical Problems in Engineering
Volume 2013 (2013), Article ID 201712, 10 pages
Research Article

RBF Neural Network of Sliding Mode Control for Time-Varying 2-DOF Parallel Manipulator System

Electrical and Information Engineering institute, Jiangsu University of Technology, Changzhou, Jiangsu, 213001, China

Received 17 September 2013; Revised 16 October 2013; Accepted 16 October 2013

Academic Editor: Rongni Yang

Copyright © 2013 Haizhong Chen and Songlin Wo. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper presents a radial basis function (RBF) neural network control scheme for manipulators with actuator nonlinearities. The control scheme consists of a time-varying sliding mode control (TVSMC) and an RBF neural network compensator. Since the actuator nonlinearities are usually included in the manipulator driving motor, a compensator using RBF network is proposed to estimate the actuator nonlinearities and their upper boundaries. Subsequently, an RBF neural network controller that requires neither the evaluation of off-line dynamical model nor the time-consuming training process is given. In addition, Barbalat Lemma is introduced to help prove the stability of the closed control system. Considering the SMC controller and the RBF network compensator as the whole control scheme, the closed-loop system is proved to be uniformly ultimately bounded. The whole scheme provides a general procedure to control the manipulators with actuator nonlinearities. Simulation results verify the effectiveness of the designed scheme and the theoretical discussion.