基于遗传算法的开关磁阻电机T-S模糊模型

GA Based T-S Type Model of Switched Reluctance Motor

开关磁阻电机(SRM)磁路高度饱和及双凸极结构使相绕组的磁链是转子位置和相电流的非线性函数。建立这一非线性映射是准确解算SRM特性的基础。采用Takagi-Sugeno(T-S)模糊逻辑来建立开关磁阻电机的非线性模型。所建模型具有结构简单、训练周期少、运算速度快、鲁棒性强的特点。为提高模型的精度,模型的参数应优化。但本模型目标函数的梯度信息很难得到,这样传统的基于梯度信息的优化方法很难被用来优化模型的参数,为此采用遗传算法来优化模型参数。遗传算法是一种并行、随机但直接进化最适合个体且不依赖计算局部导数来引导搜索过程的一种优化算法。模型输出数据与实测数据和泛化样本数据十分接近。仿真的电流波形与实测的电流波形很吻合,表明所建立的模型具有精度高、泛化能力强、运算速度快、鲁棒性强的特点。

Flux linkage of switch reluctance motor （SRM） is in nonlinear function of both rotor position and phase current. Establishing this nonlinear mapping is the basis to compute the mathematical equations of SRM accurately. In this paper, the Takagi-Sugeno （T-S） type fuzzy logic was employed to develop the nonlinear model of SRM. the T-S type fuzzy logic had a simple structure, less training epoch, fast computational speed and a property of robustness. In order to get a high precision, the parameters should be optimized. In this paper, genetic algorithm （GA） was used to optimize the parameters of the proposed model. GA is an optimization technique that performs a parallel, stochastic, but directed search to evolve the most fit population, but not relay on computing local derivatives to guide the search process. Compared with the training data and generalization test data, the output data of the developed model were in good agreement with those data. The simulated current wave was also in good agreement with the measured current wave. This proved that the model developed in this paper had high accuracy, strong generalization ability, fast computational speed and characteristic of robustness.