A Self-Optimization Commutation Correction Strategy Integrating Model Calculation and Tabu Search for High-Speed BLDCM

To overcome the limitations of existing sensorless commutation control in achieving phase synchronization between stator current and back-EMF, this paper proposes a self-optimizing commutation correction strategy for high-speed brushless DC motor driven by quasi-current source inverter (QCSI). Considering factors such as winding impedance characteristics, filter circuit phase shift, and control loop delay, a more accurate mathematical model for the commutation compensation angle is established. Then, with the goal of minimizing the inductor current in the QCSI, a tabu search strategy balancing exploration and utilization is developed to obtain the optimal commutation compensation angle online. On this basis, a self-optimizing scheme by integrating model calculation and tabu search is constructed. The advantages of this integration are as follows: firstly, it improves the optimization speed and quality of the commutation compensation angle and avoids generating low-quality solutions that significantly reduce control performance; secondly, it not only ensures quick correction response speed during transient operation, but also can adapt to changes in working conditions during long-term steady-state operation, enhancing system robustness. The proposed strategy can obtain the optimal compensation angle that minimizes the inductor current under different steady-state conditions, ensuring true synchronization between stator current and back-EMF, and achieving maximum torque-to-current ratio control.