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JACIII Vol.17 No.4 pp. 637-646
doi: 10.20965/jaciii.2013.p0637
(2013)

Paper:

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Nonlinear Friction Estimation in Elastic Drive Systems Using a Dynamic Neural Network-Based Observer

Amir Hossein Jafari*, Rached Dhaouadi**, and Ali Jhemi**

*School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK, USA

**College of Engineering, American University of Sharjah, Sharjah, United Arab Emirates

Received:
November 26, 2012
Accepted:
May 16, 2013
Published:
July 20, 2013
Creative Commons License
Keywords:
recurrent neural network, elastic drive system, friction, neural network observer, control
Abstract
This paper presents a neural-network based observer for nonlinear elastic drive systems. The proposed nonlinear observer uses a Diagonal Recurrent Neural Network (DRNN) combined with the dynamics of a linear Two-Mass-Model (2MM) system to identify nonlinear characteristics of the drive system such as Coulomb and nonlinear viscous friction torques. Theoretical analysis of the proposed neural-network based observer, including the neural network structure and the training algorithm convergence, are presented and discussed. Simulation results are confirmed experimentally using a 2MM system setup.
Cite this article as:
A. Jafari, R. Dhaouadi, and A. Jhemi, “Nonlinear Friction Estimation in Elastic Drive Systems Using a Dynamic Neural Network-Based Observer,” J. Adv. Comput. Intell. Intell. Inform., Vol.17 No.4, pp. 637-646, 2013.
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