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


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

November 26, 2012
May 16, 2013
July 20, 2013
recurrent neural network, elastic drive system, friction, neural network observer, control

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:
Amir Hossein Jafari, Rached Dhaouadi, and Ali 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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Last updated on Feb. 25, 2021