Please use this identifier to cite or link to this item:
Title: Intelligent friction modeling and compensation using neural network approximations
Authors: Huang, S. 
Tan, K.K. 
Keywords: Dynamical friction
learning control
neural network (NN) control
Issue Date: Aug-2012
Citation: Huang, S., Tan, K.K. (2012-08). Intelligent friction modeling and compensation using neural network approximations. IEEE Transactions on Industrial Electronics 59 (8) : 3342-3349. ScholarBank@NUS Repository.
Abstract: In this paper, we consider the friction compensation problem for a class of mechanical systems. The friction behavior is described by a nonlinear dynamical model. Since it is difficult to know the nonlinear parts in the frictional model accurately, two neural networks (NNs) are employed in the proposed intelligent controller. Due to the learning capability of the NNs, the designed NN controller can compensate the effects of the nonlinear friction. Stability of the thus proposed learning control system is guaranteed by a rigid proof. Simulation and experimental results are provided to verify the effectiveness of the proposed intelligent scheme. © 2012 IEEE.
Source Title: IEEE Transactions on Industrial Electronics
ISSN: 02780046
DOI: 10.1109/TIE.2011.2160509
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Google ScholarTM



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