Study on Application of RBF Neural Network in Control of Giant Magnetostrictive Actuator

Author

Xie, Xiangrong ; He, Qiwei ; Hu, ShiFeng

Author_Institution

Coll. of Naval Archit. & Power, Naval Univ. of Eng., Wuhan, China

Volume

2

fYear

2010

fDate

26-28 Aug. 2010

Firstpage

280

Lastpage

284

Abstract

For magnetostrictive actuator (GMA) inherent hysteretic, a new real time hysteresis compensation method consisting of radial basis function neural network (RBF) feedforward and PID feedback controller is presented to achieve the position tracking control of GMA. Simulation results show the control strategy is effective for GMA hysteresis which is changed by the input signal, it can set up the hysteresis inverse model of GMA, thus eliminate the influence of nonlinear hysteresis and achieve high precision control of displacement GMA.

Keywords

displacement control; electromagnetic actuators; feedback; feedforward; giant magnetoresistance; magnetic hysteresis; magnetostrictive devices; neurocontrollers; position control; radial basis function networks; three-term control; PID feedback controller; RBF neural network; feedforward controller; giant magnetostrictive actuator control; hysteresis inverse model; position tracking control; radial basis function neural network; real time hysteresis compensation method; Actuators; Analytical models; Artificial neural networks; Hysteresis; Inverse problems; Magnetic hysteresis; Magnetostriction; active vibration control; giant magnetostrictive actuator; hysteresis nonlinearity; inverse hysteretic operator; radical basis function;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2010 2nd International Conference on

Conference_Location

Nanjing, Jiangsu

Print_ISBN

978-1-4244-7869-9

Type

conf

DOI

10.1109/IHMSC.2010.170

Filename

5590991