基于GMA宏微精密驱动器的磁滞非线性模型与参数辨识

doi:10.3901/JME.2025.17.245

摘要/Abstract

摘要： 音圈电机与超磁致伸缩驱动器(Giant magnetostrictive actuator, GMA)同属电磁驱动，电磁兼容性好，优势互补，将二者结构嵌套融合，实现宏微集成化。如何准确描述宏微复合驱动器中微动系统超磁致伸缩材料(Giant magnetostrictive material, GMM)的磁滞非线性、建立及辨识磁滞非线性模型是提高驱动器定位精度的关键，基于经典J-A模型，综合了微驱动器内部磁、热、力等多物理场因素以及宏动磁场的影响，构建了宏微驱动器中GMA的多场耦合理论模型。针对磁滞模型中的参数辨识问题，提出采用天牛须搜索-粒子群优化(BAS-PSO)混合算法实现，该算法将粒子群中的粒子转化为天牛个体，赋予粒子天牛须搜索的能力，集合了BAS的搜索速度及PSO的精细搜索能力，并引入自适应算法更新PSO算法中的粒子群权重w，改进了全局寻优能力和局部寻优能力。通过模拟结果与实测结果的对比，验证了该算法在磁性材料磁滞特性模型研究中的有效性和实用性，为实现驱动器的高精度定位奠定基础。

关键词: 超磁致伸缩, 多场耦合模型, 磁滞非线性, 参数辨识, 天牛须搜索粒子群算法

Abstract: Voice coil motors and giant magnetostrictive actuators (GMA) both belong to electromagnetic drive, with good electromagnetic compatibility and complementary advantages. The two structures are nested and integrated to achieve macro and micro integration. How to accurately describe the hysteresis nonlinearity of the giant magnetostrictive material (GMM) in the micro motion system of the macro micro composite actuator, and establish and identify the hysteresis nonlinearity model is the key to improving the positioning accuracy of the actuator. Based on the classic J-A model, this study integrates multiple physical field factors such as magnetic, thermal, and force inside the micro actuator, as well as the influence of the macro magnetic field, and constructs a multi field coupling theoretical model of GMA in the macro micro actuator. Aiming at the parameter identification problem in the hysteresis model, a hybrid algorithm of particle swarm optimization (BAS-PSO) is proposed. This algorithm converts particles in the particle swarm into individuals of the beetle, endowing them with the ability to search. It combines the search speed of BAS and the fine search ability of PSO, and introduces an adaptive algorithm to update the particle swarm weights in the PSO algorithm, improving the global and local optimization abilities. By comparing the simulation results with the measured results, the effectiveness and practicality of the algorithm in the study of magnetic material hysteresis characteristics models have been verified, laying the foundation for achieving high-precision positioning of actuators.

Key words: giant magnetostrictive material(GMM), multi field coupling model, magnetic hysteresis nonlinearity, parameter identification, particle swarm optimization based on beetle antennae search (BAS-PSO) algorithm

中图分类号:

TP27

解甜, 彭宣, 张梦哲, 王传礼, 徐壮. 基于GMA宏微精密驱动器的磁滞非线性模型与参数辨识[J]. 机械工程学报, 2025, 61(17): 245-254.

XIE Tian, PENG Xuan, ZHANG Mengzhe, WANG Chuanli, XU Zhuang. Magnetic Hysteresis Nonlinear Model and Parameter Identification Based on GMA Macro-micro Precision Actuator[J]. Journal of Mechanical Engineering, 2025, 61(17): 245-254.

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