TY - GEN
T1 - Neural network based inverse control of systems with hysteresis
AU - Ma, Tao
AU - Chen, Jie
AU - Chen, Wenjie
AU - Deng, Fang
PY - 2008
Y1 - 2008
N2 - A model of piezoelectric actuator with hysteresis has been built in this paper with Prandtle-Ishlinskii model. After that, a radial basis function (RBF) neural network based adaptive inverse control scheme for nonlinear systems with unknown hysteresis nonlinearity is developed. A nonlinear filter based on RBF neural networks is used in hysteresis inverse plant modeling. We use the inverse model as the controller to control the piezoelectric actuator model directly. The simulation results show that the method interposed in this paper can restrain the hysteresis effect to lower than 1.25%.
AB - A model of piezoelectric actuator with hysteresis has been built in this paper with Prandtle-Ishlinskii model. After that, a radial basis function (RBF) neural network based adaptive inverse control scheme for nonlinear systems with unknown hysteresis nonlinearity is developed. A nonlinear filter based on RBF neural networks is used in hysteresis inverse plant modeling. We use the inverse model as the controller to control the piezoelectric actuator model directly. The simulation results show that the method interposed in this paper can restrain the hysteresis effect to lower than 1.25%.
UR - http://www.scopus.com/inward/record.url?scp=60749095691&partnerID=8YFLogxK
U2 - 10.1109/MESA.2008.4735686
DO - 10.1109/MESA.2008.4735686
M3 - Conference contribution
AN - SCOPUS:60749095691
SN - 9781424423682
T3 - 2008 IEEE/ASME International Conference on Mechatronics and Embedded Systems and Applications, MESA 2008
SP - 353
EP - 356
BT - 2008 IEEE/ASME International Conference on Mechatronics and Embedded Systems and Applications, MESA 2008
T2 - 2008 IEEE/ASME International Conference on Mechatronics and Embedded Systems and Applications, MESA 2008
Y2 - 12 December 2008 through 15 December 2008
ER -