Parameter Identification of Displacement Model for Giant Magnetostrictive Actuator Using Differential Evolution Algorithm

Based on Jiles–Atherton theory and the quadratic law, a displacement model for giant magnetostrictive actuators (GMA) has been developed. The Runge–Kutta method is used to solve nonlinear differential equation of hysteresis in segmented magnetic field. Aiming at problem that parameters are coupled with each other difficult estimate, heuristic intelligent search algorithm-differential evolution algorithm (DE) employed implement parameter identification. In order verify effectiveness algorithm, comparative studies genetic (GA) particle swarm optimization (PSO) applied identification performed. simulation results demonstrate advantages requiring few control variables, fast convergence speed, stable identified results, excellent repeatability. Furthermore, experimental output displacements calculated from great agreement measured values. Accordingly, DE can identify satisfactory accuracy reliability.