Paper:

# 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

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.

*J. Adv. Comput. Intell. Intell. Inform.*, Vol.17, No.4, pp. 637-646, 2013.

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