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Volume 2017, Article ID 1895897, 14 pages
Review Article

A Brief Review of Neural Networks Based Learning and Control and Their Applications for Robots

1Key Laboratory of Autonomous Systems and Networked Control, College of Automation Science and Engineering, South China University of Technology, Guangzhou, China
2Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming, China
3School of Engineering and Material Sciences, Queen Mary University of London, London, UK
4School of Engineering and Informatics, University of Sussex, Brighton, UK
5National Institute of Advanced Industrial Science and Technology, Tokyo, Japan

Correspondence should be addressed to Chenguang Yang; gro.eeei@gnayc

Received 15 June 2017; Accepted 1 October 2017; Published 31 October 2017

Academic Editor: Thierry Floquet

Copyright © 2017 Yiming Jiang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


As an imitation of the biological nervous systems, neural networks (NNs), which have been characterized as powerful learning tools, are employed in a wide range of applications, such as control of complex nonlinear systems, optimization, system identification, and patterns recognition. This article aims to bring a brief review of the state-of-the-art NNs for the complex nonlinear systems by summarizing recent progress of NNs in both theory and practical applications. Specifically, this survey also reviews a number of NN based robot control algorithms, including NN based manipulator control, NN based human-robot interaction, and NN based cognitive control.