基于混合遗传算法的开关磁阻电机模型参数辨识

将开关磁阻电机（SRM）的建模问题作为一类非线性约束优化问题进行参数辨识研究，对模拟退火遗传算法进行了改进，提出一种带有退火精确罚函数的自适应混合遗传算法（HGA），给出了算法的具体实现方法．建立了基于DSPTMS320F2812的磁链特性检测系统，通过实验获取开关磁阻电机的磁化曲线族，利用改进的混合遗传算法在测得的实验数据基础上对电机模型进行参数辨识．基于辨识得到的模型，对电机在两种不同工况下的运行特性分别进行了研究，仿真结果与实验结果的对比验证了该方法的有效性和准确性．辨识得到的电机模型可以作为电机性能估计及优化控制的基础．

Parameter Identification for Modeling Switched Reluctance Motor Based on Hybrid Genetic Algorithm

Parameter identification for optimization problems with nonlinear constraint conditions was introduced into the modeling for switched reluctance motors （SRM）. A simulated annealing genetic algorithm was improved,and an adaptive hybrid genetic algorithm （HGA） with annealing exact penalty function with detailed presentation of its realization was presented. A setup based on DSP TMS320F2812 for acquiring the magnetization curves of SRM was developed. According to the algorithm proposed here ,parameters of the flux linkage model were identified based on the experimental data,and then the operation characteristics for two load conditions were obtained respectively by simulation. The comparison between the simulated and experimental results was completed,from which the validity and accuracy of this method are verified. The identified model of the SRM can be used in motor performances predicting and optimized control strategy development.