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

Abstract

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