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

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 t...

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Bibliographic Details
Published in:Mathematical problems in engineering 2013-01, Vol.2013 (2013), p.1-10
Main Authors: Chen, Haizhong, Wo, Songlin
Format: Article
Language:English
Subjects:
Actuators
Basis functions
Compensators
Computer simulation
Control algorithms
Control stability
Control systems
Controllers
Manipulators
Network control
Neural networks
Nonlinear systems
Nonlinearity
Parallel degrees of freedom
Radial basis function
Robot arms
Robots
Sliding mode control
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Summary: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.
ISSN:1024-123X
1563-5147
DOI:10.1155/2013/201712