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This research aims to enhance motor position control by reducing the required switching gain using RBF-NN.

January 6, 2026ActuatorsOpen Access

RBF-NN Supervisory Integral Sliding Mode Control for Motor Position Tracking with Reduced Switching Gain

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Simulation reveals RBF-NN control enhances motor tracking in uncertain conditions, suggesting robustness in control systems.

This research aims to enhance motor position control by reducing the required switching gain using RBF-NN.
Proposed an RBF-NN-based supervisory learning approach.
Conducted comparative simulations and laboratory experiments.
Tested under varying system parameter uncertainties and sinusoidal disturbances.
Demonstrated reduced switching gain and improved tracking error norms.
Showed superior performance over conventional ISMC and DOBC-based controllers.

Son et al. (2026) studied this question.

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