Coupling model identification of giant magnetostrictive actuator using invasive weed algorithm

The hysteresis model of giant magnetostrictive actuator, considering the hysteresis loss and dynamic stress, can comprehensively reveal the electric, magnetic, mechanical and thermal multi-field coupling effect. It is often, however, difficult to accurately identify the nonlinear model by the experiment. The intelligent IWO (invasive weed optimization) with a fierce competition mechanism and strong search ability is very suitable for solving the problem of multi-objective physical parameters identification. Nevertheless, the number of seeds is linearly generated in traditional IWO and the distribution variance is lack of adaptability as well, which greatly affects the algorithm convergence speed and model recognition accuracy. Therefore, an improved algorithm with nonlinear propagation and distribution was proposed and applied to the model parameters identification of giant magnetostrictive actuator. The experiment exhibits that the improved algorithm has stronger noise suppression ability, which can accurately identify the physical parameters of the hysteresis nonlinear model with noise signal, and the errors between model predictions and experimental data are much smaller, thus the identified parameters can make the hysteresis nonlinear model comprehensively describe the actuator multi-field coupling mechanism and dynamic characteristics. © 2018, NUDT Press. All right reserved.