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使用改进的粒子群优化算法辨识Jiles-Atherton模型参数。针对J-A模型对超磁致伸缩致动器(giant magnetostrictive actuator,GMA)迟滞特性建模中磁化参数互相嵌套难以辨识的特点,改进磁滞模型并建立了考虑超磁致伸缩材料磁机耦合特性的动态磁滞模型;为了克服普通粒子群算法实际求模型参数时计算量大,运行时间长的缺点,提出基于粒子群算法和遗传算法的改进算法--带交叉因子的粒子群优化算法,将模型仿真所求的磁化强度和实验测得的磁化强度的差值的平方作为适应度函数,并结合最小二乘法思想对J-A模型的几个参数进行辨识;最后,在Matlab 7.0上进行仿真,给出了模型辨识后的结果。在不同预压力和驱动频率下的仿真结果与GMA已有实验数据进行对比,验证得出辨识后的模型可较好地与实验数据拟合,磁致伸缩位移误差在5%以内。
The Jiles-Atherton model parameters are identified using an improved particle swarm optimization algorithm. Aiming at the characteristics of JA model that the magnetization parameters are hard to identify in the modeling of the hysteresis characteristics of giant magnetostrictive actuators (GMA), the hysteresis model is improved and the magnetomechanical coupling characteristics of the giant magnetostrictive actuator In order to overcome the shortcomings of large computational complexity and long running time of ordinary particle swarm optimization algorithm, this paper proposes an improved particle swarm optimization algorithm based on particle swarm optimization (GA) and genetic algorithm The square of the difference between the magnetization obtained from the model simulation and the experimentally measured magnetization is taken as the fitness function, and several parameters of the JA model are identified by combining with the idea of least square method. Finally, Out of the model identification results. The simulation results under different pre-pressure and driving frequency are compared with the existing experimental data of GMA. The results show that the model can be well fitted with the experimental data, and the magnetostrictive displacement error is within 5%.