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Cluster Computing

Erschienen in:

30.12.2017

Trajectory tracking control of robot manipulator based on RBF neural network and fuzzy sliding mode

verfasst von: Fei Wang, Zhi-qiang Chao, Lian-bing Huang, Hua-ying Li, Chuan-qing Zhang

Erschienen in: Cluster Computing | Sonderheft 3/2019

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Abstract

Aimed at the nonlinearity and uncertainty of the manipulator system, a RBF (radial basis function) neural network-based fuzzy sliding-mode control method was proposed in this paper, in order to make the manipulator track the given trajectory at an ideal dynamic quality. In this method, the equivalent part of the sliding-mode control is approximated by the RBF neural network, in which no model information is required. Meanwhile, a fuzzy controller is developed to make adaptive adjustment of the sliding-mode control’s switching gains according to the distance between the current motor point and the sliding-mode surface, thus effectively the problem of chattering is solved. This method has, to some extent, improved the performance of response and tracking, and reduced the time of adjustment and chattering of input control. The system stability is verified by Lyapunov’s theorem. The simulation result suggests that the algorithm designed for the three-degree-of-freedom (3DOF) manipulator system is effective.

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Titel: Trajectory tracking control of robot manipulator based on RBF neural network and fuzzy sliding mode
verfasst von: Fei Wang
Zhi-qiang Chao
Lian-bing Huang
Hua-ying Li
Chuan-qing Zhang
Publikationsdatum: 30.12.2017
Verlag: Springer US
Erschienen in: Cluster Computing / Ausgabe Sonderheft 3/2019
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI: https://doi.org/10.1007/s10586-017-1538-4

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