Investigation of Backlash and Friction Nonlinearities in a 1-DoF Electromechanical System Based on Experimental Data

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.