Modeling for giant magnetostrictive actuators with rate-dependent hysteresis based on Hammerstein-like system by using LS-SVM

The rate-dependent hysteresis in giant magnetostrictive materials is a major impediment to the application of such material in actuators. In this paper, a Hammerstein-like model based on Least Square Support Vector Machines (LS-SVM) is proposed to model the rate-dependent hysteresis system. We show that it is possible to construct a unique dynamic model in a given frequency range for a rate-dependent hysteresis system using the sinusoidal scanning signals as the training set of signals for the linear dynamic subsystem of the Hammerstein-like model, which guarantees an outstanding generalization ability of frequency. The LS-SVM parameters influencing the model accuracy are determined through the intelligent particle swarm optimization (PSO) and the dimension of input data is also discussed. Simulations on a giant magnetostrictive actuator (GMA) verify both the effectiveness and practicality of the proposed modeling method.

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