Title :
Hysteresis compensation for giant magnetostrictive actuators using dynamic recurrent neural network
Author :
Cao, Shuying ; Wang, Boweng ; Zheng, Jiaju ; Huang, Wenmei ; Weng, Ling ; Yan, Weili
Author_Institution :
Sch. of Electr. Eng. & Automatization, Hebei Inst. of Technol., Tianjin
fDate :
4/1/2006 12:00:00 AM
Abstract :
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
Keywords :
PD control; compensation; electromagnetic actuators; giant magnetoresistance; magnetic hysteresis; magnetostrictive devices; recurrent neural nets; DRNN inverse compensator; PD controller; dynamic recurrent neural network; giant magnetostrictive actuators; inverse hysteresis model; online hysteresis compensation control strategy; precision position tracking; proportional derivative controller; Actuators; Frequency; Fuzzy control; Inverse problems; Magnetic hysteresis; Magnetostriction; PD control; Proportional control; Recurrent neural networks; Saturation magnetization; Dynamic recurrent neural network (DRNN); hysteresis; inverse compensator; magnetostrictive actuator (MA);
Journal_Title :
Magnetics, IEEE Transactions on
DOI :
10.1109/TMAG.2006.871464
