Predictive Current Control of Permanent Magnet Synchronous Motor Based on Parameter Identification

Abstract

Aiming at the unsatisfactory accuracy and speed of traditional parameter identification methods for permanent magnet synchronous motors (PMSMs), a parameter identification method based on an improved hunter prey optimization (HPO) algorithm (Tent chaotic initialization and firefly algorithm HPO (TF-HPO)) was proposed. Using the Tent chaotic map, the initial individuals are evenly distributed to enrich their diversity, and the population position is updated using the firefly perturbation algorithm. Simulation and practical experiments show that compared with unmodified algorithm, the improved algorithm has faster convergence speed and higher recognition accuracy, and can effectively identify the parameters of the motor. On this basis, deadbeat predictive current control is implemented, effectively eliminating current static errors and improving the accuracy and stability of the current control system, and can effectively suppress motor torque ripple and current harmonics caused by parameter deviations.