What question did this study set out to answer?

This study aims to develop and validate a robust position controller for BLDC motors to handle nonlinear disturbances.

August 2, 2026Open Access

Design and Experimental Validation of an ISMC-Based Position Controller with Supervisory RBF Neural Network and PIO for BLDC Motor Systems

YSYoung Ik SonMyongji University HCHaneul ChoMyongji University JKJunho KangMyongji University

Key Points

This study aims to develop and validate a robust position controller for BLDC motors to handle nonlinear disturbances.
Developed a control method integrating ISMC, RBF-NN, and PIO for effective position control.
Utilized comparative simulations with a LuGre friction model and validated through hardware experiments.
Analyzed system stability using Lyapunov methods under various input conditions.
The proposed controller significantly reduces residual tracking errors compared to conventional ISMC, ISMC+RBF, and IMP+PIO controllers.
Showed effective disturbance compensation without excessive control input, enhancing overall robustness.
Demonstrated successful operation under both steady and time-varying load conditions.

Abstract

This paper proposes a robust position control method that integrates integral sliding mode control (ISMC), a radial basis function neural network (RBF–NN), and a proportional–integral observer (PIO) for a BLDC motor system subject to harmonic-drive loads under nonlinear friction, model uncertainty, and time-varying disturbances. In the proposed structure, the RBF–NN suppresses the major nonlinear equivalent disturbance components online, while the PIO estimates the residual disturbance remaining after the RBF–NN action. The PIO residual-disturbance estimate is further incorporated into the RBF–NN weight-update law to provide residual-disturbance information to the RBF–NN adaptation and improve the coordination between the two compensation mechanisms. The closed-loop stability of the proposed controller is demonstrated using Lyapunov analysis. The proposed method is validated through comparative simulations using an identified LuGre friction model and hardware experiments under step and sinusoidal reference inputs and disturbances, including baseline and additional-load conditions with increased model uncertainty. The results show that the proposed controller reduces residual tracking errors more effectively than conventional ISMC, ISMC+RBF, and internal model principle (IMP)+PIO controllers without requiring excessive control input, demonstrating its practical robustness in complex BLDC motor drive systems.

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