Auto Associative Kernel Regression Data-Driven Method for Permanent Magnet Synchronous Motor Control

With the progress of science and technology, the demand for motor control technology is growing. This paper proposes a data-driven motor control strategy based on auto associative kernel regression (AAKR) algorithm, which aims to optimize the control method for permanent magnet synchronous motor (PMSM) and improve performance and efficiency by analyzing operational data. Through theoretical analysis and MATLAB/Simulink simulation, the strategy effectively reduces the current fluctuation and torque ripple, indicating that the AAKR algorithm has an effect on the improvement of motor control performance. The successful implementation of this method provides a new way for to optimize other motor control strategies.