Trajectory Tracking Control of an Industrial Robot Manipulator Using Fuzzy SMC with RBFNN

被引:1
作者
Gokhan, Ayca A. K. [1 ]
Cansever, Galip [2 ]
Delibasi, Akin [2 ]
机构
[1] Marmara Univ, Vocat Sch Tech Sci, TR-34722 Istanbul, Turkey
[2] Yildiz Tech Univ, Control & Automat Engn, TR-34220 Istanbul, Turkey
来源
GAZI UNIVERSITY JOURNAL OF SCIENCE | 2015年 / 28卷 / 01期
关键词
Neural Network; Fuzzy Logic; Sliding Mode Control; Robot Control;
D O I
暂无
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
One of the main problems associated with Sliding Mode Control (SMC) is that a whole knowledge of the system dynamics and system parameters is required to compute the equivalent control. Neural networks are popular tools for computing the equivalent control. In fuzzy SMC with Radial Basis Function Neural Network (RBFNN), a Lyapunov function is selected for the design of the SMC and RBFNN is proposed to compute the equivalent control. The weights of the RBFNN are adjusted according to an adaptive algorithm. Fuzzy logic is used to adjust the gain of the corrective control of the SMC. Proposed control method and a PID controller are tested on the Manutec-r15industrial robot manipulator. The real time implementations indicate that the proposed method can be applied to trajectory control applications of robot manipulators.
引用
收藏
页码:141 / 148
页数:8
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