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.
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