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Based on the Jiles-Atherton model and transducer's structural dynamics principle, the magnetoelastic dynamic strain model of giant magnetostrictive transducer is founded. This model takes into account the eddy current losses and the variety of stress. Simulation results are in a good agreement with the experimental ones. This indicates that the dynamic strain model can characterize the magnetization intensity and strain behavior at different frequency. The proposed model is suitable to describe the relation between the applied field and the output vibration at a wide range of exciting frequency. Index Terms--Bode diagram, dynamic model, eddy current effects, giant magnetostrictive transducer.

According to the hysteresis characteristics of the giant magnetostrictive actuator (MA), a dynamic recurrent neural network (DRNN) is constructed as the inverse hysteresis model of the MA, and an on-line hysteresis compensation control strategy combining the DRNN inverse compensator and a proportional derivative (PD) controller is used for precision position tracking of the MA. Simulation results validate the excellent performances of the proposed strategy. Index Terms--Dynamic recurrent neural network (DRNN), hysteresis, inverse compensator, magnetostrictive actuator (MA).

This paper establishes a simple and novel dynamic hysteresis model for giant magnetostrictive actuator by considering the eddy current loss, anomalous loss and structural dynamic mechanical behavior of the actuator. To obtain parameters of the model, a hybrid genetic algorithm is proposed. Comparisons between the experimental and calculated results show the validity and practicability of the model. Index Terms--Dynamic hysteresis model, hybrid genetic algorithm (HGA), Jiles--Atherton model, magnetostrictive actuator (MA).

The dynamic model with hysteretic nonlinearity for magnetostrictive actuators is developed, combined with Jiles-Atherton model. The simulation and experimental results demonstrate the effectiveness of the model and parameter identification approach.