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

被引：6
|
作者：
Liu, Fei [1 ]
Fan, Shaosheng [1 ]
机构：
[1] Changsha Univ Sci & Technol, Elect & Informat Engn Dept, Changsha, Hunan, Peoples R China
来源：
2009 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND COMPUTATIONAL INTELLIGENCE, VOL II, PROCEEDINGS | 2009年
关键词：
two link robotic manipulator; sliding mode control; radial basis function neural network (RBFNN); adaptive fuzzy gain control;
D O I：
10.1109/AICI.2009.276
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
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页码：272 / 276
页数：5
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