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Journals / International Advanced Researches and Engineering Journal / 2025 / Volume: 9 Issue: 3

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

Masoud Abedinifar(İSTANBUL TEKNİK ÜNİVERSİTESİ, FEN BİLİMLERİ ENSTİTÜSÜ)0000-0002-4050-9835
Seniz Ertugrul(IZMIR UNIVERSITY OF ECONOMICS, FACULTY OF ENGINEERING)0000-0003-1766-1676
Pages
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Publication Date
2025-12-25
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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).

📌 3 Key Takeaways

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Objective & Hypothesis:The characterization of nonlinearities, specifically backlash and friction, in one-degree-of-freedom (1-DoF) electromechanical systems is essential for achieving high-precision control.
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Key Findings & Data: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.
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Conclusion & Impact: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).
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Electromechanical Systems · Mathematical Modeling · System Identification

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.

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Cite This Article

Abedinifar, M., Ertugrul, S. (2025). Investigation of backlash and friction nonlinearities in a 1-DoF electromechanical system based on experimental data. International Advanced Researches and Engineering Journal, Volume: 9 Issue: 3. https://doi.org/10.35860/iarej.1778704

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