PSO-optimized fuzzy MRAS control for permanent magnet synchronous motorized spindle

With the widespread adoption of Permanent Magnet Synchronous motorized spindles in industrial applications, the demand for their precise control has escalated significantly. However, within the realm of sensorless control strategies, traditional Model Reference Adaptive Systems (MRAS) exhibit certain shortcomings, such as the complexity of parameter tuning and a heightened sensitivity to external disturbances. Addressing the deficiencies of traditional model reference adaptive systems (MRAS) in sensorless control of permanent magnet synchronous motorized spindle, this paper introduces a speed estimation strategy based on a PSO-optimized fuzzy MRAS. Fuzzy MRAS is devised using fuzzy theory on the foundation of a stator current model-based MRAS, eliminating the need for tuning the Proportional-Integral (PI) adaptive mechanism. Furthermore, the particle swarm optimization (PSO) algorithm is applied to optimize relevant parameters within the fuzzy MRAS, enhancing its dynamic performance and robustness. Simulation experiments demonstrate that the designed PSO-optimized fuzzy MRAS observer exhibits superior tracking performance across various speed ranges and in the presence of external disturbances.