Paper Title:
Fuzzy Support Vector Machines Control for Robotic Manipulator
  Abstract

To improve the control precision of robotic manipulator, fuzzy support vector machines control method for robotic manipulator was presented based on genetic algorithm and least square algorithm. Fuzzy algorithm was used to decouple joints. Using support vector machines, fuzzy logical control of complete process and treatment of non-linear signal were realized. The parameters of controller were optimized by hybrid learning algorithm. First, least square algorithm was used for off-line optimization to form support vector machines control system. Then, genetic algorithm was used for on-line optimization to get the optimal performance parameters of support vector machines and the optimal fuzzy proportional parameters. Simulation results of a two-link manipulator demonstrated that the control method designed gets tracking effect with high precision.

  Info
Periodical
Advanced Materials Research (Volumes 204-210)
Edited by
Helen Zhang, Gang Shen and David Jin
Pages
301-305
DOI
10.4028/www.scientific.net/AMR.204-210.301
Citation
D. Q. Zhu, T. Mei, C. M. Cao, "Fuzzy Support Vector Machines Control for Robotic Manipulator", Advanced Materials Research, Vols. 204-210, pp. 301-305, 2011
Online since
February 2011
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Price
$32.00
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