Title :
Sliding Mode Control of Robot Manipulators Based on Neural Network Reaching Law
Author :
Chen, Zhimei ; Zhang, Jinggang ; Wang, Zhenyan ; Zeng, Jianchao
Author_Institution :
Taiyuan Univ. of Sci. & Technol., Taiyuan
fDate :
May 30 2007-June 1 2007
Abstract :
A new neural network sliding mode control method of robot manipulators is proposed, which is formed by incorporating sliding mode variable structure control (SMVSC) and neural network reaching law. The reaching law parameters are regulated adaptively by two feedforward neural networks (FNNs) respectively. This method converts a multi-input system into n single-input systems. Its control arithmetic is simple and easy to implement. It can not only eliminate the chattering of sliding mode control and strengthen the system robustness, but also improve the character of reaching phase. Tracking errors can promptly converge to a neighborhood of zero. The simulation results of two-degree-of-freedom robot manipulators prove the effectiveness of this scheme.
Keywords :
control engineering computing; feedforward neural nets; manipulators; variable structure systems; control arithmetic; feedforward neural networks; multi-input system; neural network reaching law; single-input systems; sliding mode variable structure control; two-degree-of-freedom robot manipulators; Arithmetic; Artificial neural networks; Automatic control; Control systems; Manipulators; Neural networks; Nonlinear control systems; Robot control; Robotics and automation; Sliding mode control; feedforward neural network; reaching law; robot manipulators; sliding mode control;
Conference_Titel :
Control and Automation, 2007. ICCA 2007. IEEE International Conference on
Conference_Location :
Guangzhou
Print_ISBN :
978-1-4244-0818-4
Electronic_ISBN :
978-1-4244-0818-4
DOI :
10.1109/ICCA.2007.4376382
