Title :
Neural Network Based Inverse Control of Systems with Hysteresis
Author :
Tao, Ma ; Jie, Chen ; Wenjie, Chen ; Fang, Deng
Author_Institution :
Dept. of Autom. Control, Inst. of Technol., Beijing
Abstract :
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%.
Keywords :
adaptive control; control nonlinearities; neurocontrollers; nonlinear control systems; piezoelectric actuators; radial basis function networks; Prandtle-Ishlinskii model; adaptive inverse control scheme; nonlinear filter; nonlinear systems; piezoelectric actuator; radial basis function neural network; unknown hysteresis nonlinearity; Adaptive control; Adaptive systems; Control systems; Hysteresis; Inverse problems; Neural networks; Nonlinear control systems; Nonlinear systems; Piezoelectric actuators; Programmable control;
Conference_Titel :
Mechtronic and Embedded Systems and Applications, 2008. MESA 2008. IEEE/ASME International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-2367-5
Electronic_ISBN :
978-1-4244-2368-2
DOI :
10.1109/MESA.2008.4735686
