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

Amir H. Jafari | Zendy; Rached Dhaouadi | Zendy; Ali Jhemi | Zendy

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

Author(s) -

Amir H. Jafari,

Rached Dhaouadi,

Ali Jhemi

Publication year - 2013

Publication title -

journal of advanced computational intelligence and intelligent informatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.172

H-Index - 20

eISSN - 1343-0130

pISSN - 1883-8014

DOI - 10.20965/jaciii.2013.p0637

Subject(s) - artificial neural network , nonlinear system , computer science , control theory (sociology) , observer (physics) , convergence (economics) , diagonal , torque , artificial intelligence , mathematics , physics , control (management) , quantum mechanics , economics , geometry , thermodynamics , economic growth

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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