Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/77518
Title: Robot manipulator control using neural networks : a survey
Authors: Jin, L
Li, S 
Yu, J
He, J 
Keywords: Cable-driven manipulators
Dual neural network
Feedforward neural network
Neural network
Parallel manipulators
Recurrent neural network
Robot arms
Robot manipulators
Winner-take-all
Issue Date: 2018
Publisher: Elsevier
Source: Neurocomputing, 2018, v. 285, p. 23-34 How to cite?
Journal: Neurocomputing 
Abstract: Robot manipulators are playing increasingly significant roles in scientific researches and engineering applications in recent years. Using manipulators to save labors and increase accuracies are becoming common practices in industry. Neural networks, which feature high-speed parallel distributed processing, and can be readily implemented by hardware, have been recognized as a powerful tool for real-time processing and successfully applied widely in various control systems. Particularly, using neural networks for the control of robot manipulators have attracted much attention and various related schemes and methods have been proposed and investigated. In this paper, we make a review of research progress about controlling manipulators by means of neural networks. The problem foundation of manipulator control and the theoretical ideas on using neural network to solve this problem are first analyzed and then the latest progresses on this topic in recent years are described and reviewed in detail. Finally, toward practical applications, some potential directions possibly deserving investigation in controlling manipulators by neural networks are pointed out and discussed.
URI: http://hdl.handle.net/10397/77518
ISSN: 0925-2312
EISSN: 1872-8286
DOI: 10.1016/j.neucom.2018.01.002
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