Data-Driven Disturbance Compensation for DC Motors

Abstract

This paper presents applications of recently developed data-driven disturbance observer [1] for DC motor control. The proposed approach enables effective compensation for external disturbances, allowing for robust control even in the presence of noise or other interfering factors. By utilizing data-driven techniques, the disturbance observer learns the system dynamics from the behavior of the system to accurately estimate and compensate for disturbances. Experimental results demonstrate the effectiveness of the proposed data-driven observer in mitigating the impact of disturbances and improving the overall control performance of the motor system. © 2023 ICROS.