Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/76824
Title: A novel recurrent neural network for manipulator control with improved noise tolerance
Authors: Li, S 
Wang, H
Rafique, MU
Keywords: Kinematic control
Noise
Recurrent neural network
Redundant manipulator
Issue Date: 2018
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on neural networks and learning systems, 2018 (article in press) How to cite?
Journal: IEEE transactions on neural networks and learning systems 
Abstract: In this paper, we propose a novel recurrent neural network to resolve the redundancy of manipulators for efficient kinematic control in the presence of noises in a polynomial type. Leveraging the high-order derivative properties of polynomial noises, a deliberately devised neural network is proposed to eliminate the impact of noises and recover the accurate tracking of desired trajectories in workspace. Rigorous analysis shows that the proposed neural law stabilizes the system dynamics and the position tracking error converges to zero in the presence of noises. Extensive simulations verify the theoretical results. Numerical comparisons show that existing dual neural solutions lose stability when exposed to large constant noises or time-varying noises. In contrast, the proposed approach works well and has a low tracking error comparable to noise-free situations.
URI: http://hdl.handle.net/10397/76824
ISSN: 2162-237X
EISSN: 2162-2388
DOI: 10.1109/TNNLS.2017.2672989
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

5
Last Week
0
Last month
Citations as of Aug 13, 2018

WEB OF SCIENCETM
Citations

5
Citations as of Aug 11, 2018

Page view(s)

9
Citations as of Aug 14, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.