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Hysteresis compensation for giant magnetostrictive actuators using dynamic recurrent neural network

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6 Author(s)
Shuying Cao ; Sch. of Electr. Eng. & Automatization, Hebei Inst. of Technol., Tianjin ; Boweng Wang ; Jiaju Zheng ; Wenmei Huang
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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

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Magnetics, IEEE Transactions on  (Volume:42 ,  Issue: 4 )