Title :
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
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;
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
DOI :
10.1109/IHMSC.2010.170
