Abstract：Permanent magnet synchronous motor is a nonlinear, highly coupled dynamic system. Its moment of inertia has a direct impact on the parameter self-tuning of the AC servo system with PMSM as the main actuator. The accurate identification of moment of inertia plays an important role in the fast and stable operation of the servo system. In view of the shortcomings of traditional inertia identification strategy, such as low identification accuracy and poor timeliness, this paper combines model reference adaptive identification strategy with improved ant colony algorithm, and proposes a new inertia identification method. Using the strong global search ability and fast convergence of ant colony algorithm, the reference model approaches the adjustable model, and the inertia estimation approaches the real value. The simulation results show that compared with the traditional method, this method has high inertia identification accuracy, fast identification speed and can meet the high speed regulation performance requirements of the servo system.
陶涛, 林荣文. 基于改进蚁群算法的永磁同步电动机参数辨识策略研究[J]. 电气技术, 2020, 21(6): 13-18.
Tao Tao, Lin Rongwen. Research on parameter identification strategy of permanent magnet synchronous motor based on improved ant colony optimization. Electrical Engineering, 2020, 21(6): 13-18.
2020, Vol. 21 
