A Neural Network Sliding Mode Controller with Application to Robotic Manipulator

Author

Peng, Jinzhu ; Wang, Yaonan ; Sun, Wei ; Liu, Yan

Author_Institution

Coll. of Electr. & Inf. Eng., Hunan Univ., Changsha

Volume

1

fYear

0

fDate

0-0 0

Firstpage

2101

Lastpage

2105

Abstract

A sliding mode control strategy compensated by neural network is proposed, and that is applied to robotic trajectory control. First, a three-layer neural network is used to compensate the uncertainties in the robotic system. Then the structure of sliding mode controller with neural network compensation and the learning algorithm of the neural network are designed based on Lyapunov theorem to guarantee the stability of the system and improve the dynamic performance of the system. The simulation results show that the proposed control strategy can not only reduce the phenomenon of chattering in effect, but also has good robustness and dynamic performance

Keywords

Lyapunov methods; compensation; learning (artificial intelligence); manipulators; neurocontrollers; position control; robust control; uncertain systems; variable structure systems; Lyapunov theorem; learning algorithm; neural network compensation; neural network sliding mode controller; robotic manipulator; robotic system; robotic trajectory control; system stability; Aerodynamics; Control systems; Friction; Manipulators; Motion control; Neural networks; Robot control; Robust control; Sliding mode control; Uncertainty; Neural Network; Robotic Trajectory Control; Sliding Mode Control;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on

Conference_Location

Dalian

Print_ISBN

1-4244-0332-4

Type

conf

DOI

10.1109/WCICA.2006.1712729

Filename

1712729