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

fYear

2008

fDate

12-15 Oct. 2008

Firstpage

353

Lastpage

356

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/MESA.2008.4735686

Filename

4735686