基于改进教与学优化算法的永磁同步电机参数辨识

Permanent Magnet Synchronous Motor Parameter Identification Based on Improved Teaching-Learning-Based Optimization

高精度辨识永磁同步电机参数是进行控制器设计的基础。针对传统永磁同步电机参数辨识方法中存在辨识速度慢、精度低等缺陷,提出了一种改进教与学优化算法对永磁同步电机进行参数辨识。该算法在教学阶段引入辅导教学机制加强教师的教学能力,提高算法收敛速度,在学习阶段,采用科目分步学习提高学员学习效率,并融入反向学习策略进行小概率变异来增加算法跳出局部最优的可能性。通过实验仿真表明,与基本教与学优化算法、粒子群算法相比,所提算法对于同时辨识定子电阻,d、q轴电感和转子磁链具有较好的收敛性和可靠性。

High accuracy identification of parameters in permanent magnet synchronous motor （PMSM） is the basis of controller design. In order to overcome the shortages of traditional identification methods such as slow speed and low identification accuracy, an improved teaching-learning-based optimization algorithm （ITLBO） was proposed to identify the permanent magnet synchronous motor parameters. In the teaching phrase, tutorial teaching mechanism was introduced to strengthen teacher＇s capacity and improved the convergence rate of algorithm, in the learning phrase, the course stepwise learning was used to improve learners＇learning efficiency. Besides, opposition-based-learning was introduced for small probability mutation, which enhanced the possibility out of local optima. The simulation result shows that the proposed algorithm has better convergence and reliability in simultaneous identification of the stator resistance, d-axis and q-axis inductance and the rotor linkage compared with teaching-learning-based optimization and particle swarm optimization.