Investigation of backlash and friction nonlinearities in a 1-DoF electromechanical system based on experimental data

Year 2025, Volume: 9 Issue: 3, 191 - 200, 25.12.2025

Masoud Abedinifar , Seniz Ertugrul

https://doi.org/10.35860/iarej.1778704

Abstract

The characterization of nonlinearities, specifically backlash and friction, in one-degree-of-freedom (1-DoF) electromechanical systems is essential for achieving high-precision control. This study presents a systematic investigation into the identification of these phenomena using a white-box modeling approach. An experimental platform, consisting of a brushed DC motor with a gearbox and a 3D-printed L-shaped load arm, was developed to generate input-output data from sinusoidal voltage excitations. A comprehensive nonlinear model, developed in MATLAB/Simulink, incorporated electrical dynamics, Coulomb and viscous friction, gravitational torque, and backlash dead-zone effects. Two complementary parameter identification methods, Nonlinear Least Squares Errors (NLSE) estimation and a Genetic Algorithm (GA), were applied to estimate the model's unknown parameters. Results demonstrated that both approaches successfully captured the dominant system dynamics; however, NLSE achieved superior accuracy in both identification (RMSE = 0.13 rad/s, R2 = 0.99) and verification (RMSE = 0.16 rad/s, R2 = 0.96) phases, compared to GA (RMSE = 0.21-0.22 rad/s, R2 = 0.94-0.97). These findings demonstrate that, with identical initialization and constraints of system parameters, a physics-based white-box model combined with NLSE provides a more reliable and precise characterization of combined backlash and friction nonlinearities than GA for the investigated 1-DoF electromechanical system and excitation conditions.

Keywords

Electromechanical Systems , Mathematical Modeling , System Identification , Backlash; , Friction

Thanks

The authors gratefully acknowledge the contributions of the FENG498 Project team, Damla Köleli, Ali Gül, Sude Kurt, and Cem Satılmış, for designing and developing the experimental setup utilized in this study, which served as the foundation for the modeling and parameter identification work presented herein.

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