Online parameter identification of a giant magnetostrictive actuator based on the dynamic Jiles–Atherton model

Abstract

Giant magnetostrictive actuator (GMA) suffers dominant hysteresis nonlinearity while working. In order to better predict its output, a dynamic model of GMA based on the J–A model is established. On the foundation of the model, an online parameter identification method is proposed. Classified into two types, some parameters are determined by physical theories, while the others are identified by a sensitivity-based identification method. Serving as the identification algorithm, the particle swarm optimization (PSO) algorithm is modified to accelerate the identification process. Moreover, to validate the effectiveness of the proposed method, an online parameter identification system is designed as well as identification software. Experimental results prove that the proposed system performs well and is fit for online identification.