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