Controller Parameter Optimization Strategy Based on Characteristic Model for Single-Winding Bearingless Flux-Switching Permanent Magnet Motor

The strong coupling of suspension force and torque in a single-winding bearingless flux-switching permanent magnet motor (SWBFSPMM) creates a complex dynamic model, complicating controller parameter tuning. The complexity and large data requirements of intelligent algorithms highlight the continued relevance of PID control, which remains dominant in high-integration and low-cost applications. Consequently, research on tuning PID controllers is crucial. This article proposes a controller parameter optimization strategy based on characteristic models. Initially, the principles governing the generation of controllable suspension force and unbalanced magnetic pull during motor operation are analyzed, leading to the development of corresponding mechanistic models. Notably, a spatially asymmetric rotor dynamic eccentricity modulation operator is introduced to accurately represent the establishment of unbalanced magnetic pull. The rotor dynamic model is then simplified, and a characteristic model for the system is established. To enhance controller parameter tuning, an online algorithm for optimization is proposed, which involves iteratively adjusting the controller parameters over a specified period, guided by the output error between the characteristic model and the actual system. This approach facilitates the determination of optimal controller parameters. Finally, experimental studies are conducted on the prototype, validating the effectiveness of the proposed method.