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Title: Robust neural network control of robotic manipulators via switching strategy (English)
Author: Yu, Lei
Author: Fei, Shumin
Author: Huang, Jun
Author: Li, Yongmin
Author: Yang, Gang
Author: Sun, Lining
Language: English
Journal: Kybernetika
ISSN: 0023-5954 (print)
ISSN: 1805-949X (online)
Volume: 51
Issue: 2
Year: 2015
Pages: 309-320
Summary lang: English
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Category: math
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Summary: In this paper, a robust neural network control scheme for the switching dynamical model of the robotic manipulators has been addressed. Radial basis function (RBF) neural networks are employed to approximate unknown functions of robotic manipulators and a compensation controller is designed to enhance system robustness. The weight update law of the robotic manipulator is based on switched multiple Lyapunov function method and the periodically switching law which is suitable for practical implementation is constructed. The proposed control scheme can guarantee that the resulting closed-loop switched system is asymptotically Lyapunov stable and the tracking error performance of the control system is well reached. Finally, a simulation example of two-link robotic manipulators is shown to illustrate the effectiveness of the proposed control method. (English)
Keyword: robotic manipulators
Keyword: switching control strategy
Keyword: RBF neural networks
Keyword: multiple Lyapunov function
MSC: 03C65
MSC: 20G40
MSC: 70E60
MSC: 93C85
idZBL: Zbl 06487081
idMR: MR3350564
DOI: 10.14736/kyb-2015-2-0309
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Date available: 2015-06-19T15:25:05Z
Last updated: 2016-01-03
Stable URL: http://hdl.handle.net/10338.dmlcz/144300
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