Journal of System Simulation
Abstract
Abstract: A fuzzy controller based on SVR learning is proposed for uncalibrated robot visual servoing. In this paper, a fuzzy controller is used to directly construct the nonlinear mapping between image features and robot joint motion. The fuzzy basis function of the fuzzy controller is taken as the kernel function of an SVR and the equivalent relationship between the SVR and the fuzzy controller is established. The learned support vector from the SVR is used as the rule of the fuzzy controller. Since all rules are learned from the data, there is no need to manually design the rules. The proposed method fully utilizes the good generalization ability of SVR in small sample learning, and the experimental results show that the proposed visual servoing controller has good performance in precision and convergence.
Recommended Citation
Zhang, Xianxia; Zhang, Jinqiang; Li, Zhiyuan; Ma, Shiwei; and Yang, Banghua
(2020)
"Visual Feedback Fuzzy Control for a Robot Manipulator Based on SVR Learning,"
Journal of System Simulation: Vol. 32:
Iss.
10, Article 18.
DOI: 10.16182/j.issn1004731x.joss.2020-FZ0337E
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol32/iss10/18
First Page
1997
Revised Date
2020-06-11
DOI Link
https://doi.org/10.16182/j.issn1004731x.joss.2020-FZ0337E
Last Page
2009
CLC
TP24
Recommended Citation
Zhang Xianxia, Zhang Jinqiang, Li Zhiyuan, Ma Shiwei, Yang Banghua. Visual Feedback Fuzzy Control for a Robot Manipulator Based on SVR Learning[J]. Journal of System Simulation, 2020, 32(10): 1997-2009.
DOI
10.16182/j.issn1004731x.joss.2020-FZ0337E
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