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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