Adaptive RBFNN Based Fuzzy Sliding Mode Control for Two Link Robot Manipulator

Author

Liu, Fei ; Fan, Shaosheng

Author_Institution

Dept. of Electr. & Inf. Eng., Changsha Univ. of Sci. & Technol., Changsha, China

Volume

2

fYear

2009

fDate

7-8 Nov. 2009

Firstpage

272

Lastpage

276

Abstract

A adaptive Radial basis function neural network (RBFNN) based fuzzy sliding mode control scheme for two link robot manipulator is proposed in this paper. In the scheme, RBFNN is used to approximate system dynamic, the weights of the RBFNN are changed according to adaptive algorithm to ensure the system state hitting the sliding surface and sliding along it. In order to guarantee the stability and the convergence of the system, the sliding mode control gain is adjusted by the adaptive fuzzy systems to compensate the network approximation error and the external disturbances. The simulation results demonstrate that the control scheme is effective.

Keywords

adaptive systems; approximation theory; fuzzy control; manipulators; radial basis function networks; variable structure systems; adaptive RBFNN; adaptive algorithm; adaptive fuzzy systems; adaptive radial basis function neural network; approximate system dynamic; fuzzy sliding mode control; network approximation error; sliding mode control gain; sliding surface; system state hitting; two link robot manipulator; Adaptive algorithm; Adaptive control; Fuzzy control; Fuzzy neural networks; Manipulator dynamics; Programmable control; Radial basis function networks; Robots; Sliding mode control; Stability; adaptive fuzzy gain control; radial basis function neural network (RBFNN); sliding mode control; two link robotic manipulator;

fLanguage

English

Publisher

ieee

Conference_Titel

Artificial Intelligence and Computational Intelligence, 2009. AICI '09. International Conference on

Conference_Location

Shanghai

Print_ISBN

978-1-4244-3835-8

Electronic_ISBN

978-0-7695-3816-7

Type

conf

DOI

10.1109/AICI.2009.276

Filename

5375938