Neural network based inverse control of systems with hysteresis

Tao Ma; Jie Chen; Wenjie Chen; Fang Deng

doi:10.1109/MESA.2008.4735686

Neural network based inverse control of systems with hysteresis

Tao Ma^*, Jie Chen, Wenjie Chen, Fang Deng

^*Corresponding author for this work

School of Automation

Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Citations (Scopus)

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%.

Original language	English
Title of host publication	2008 IEEE/ASME International Conference on Mechatronics and Embedded Systems and Applications, MESA 2008
Pages	353-356
Number of pages	4
DOIs	https://doi.org/10.1109/MESA.2008.4735686
Publication status	Published - 2008
Event	2008 IEEE/ASME International Conference on Mechatronics and Embedded Systems and Applications, MESA 2008 - Beijing, China Duration: 12 Dec 2008 → 15 Dec 2008

Publication series

Name	2008 IEEE/ASME International Conference on Mechatronics and Embedded Systems and Applications, MESA 2008

Conference

Conference	2008 IEEE/ASME International Conference on Mechatronics and Embedded Systems and Applications, MESA 2008
Country/Territory	China
City	Beijing
Period	12/12/08 → 15/12/08

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10.1109/MESA.2008.4735686

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Ma, T., Chen, J., Chen, W., & Deng, F. (2008). Neural network based inverse control of systems with hysteresis. In 2008 IEEE/ASME International Conference on Mechatronics and Embedded Systems and Applications, MESA 2008 (pp. 353-356). Article 4735686 (2008 IEEE/ASME International Conference on Mechatronics and Embedded Systems and Applications, MESA 2008). https://doi.org/10.1109/MESA.2008.4735686

@inproceedings{8a87b1440de7464d8d49d7eb0f574eb0,

title = "Neural network based inverse control of systems with hysteresis",

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%.",

author = "Tao Ma and Jie Chen and Wenjie Chen and Fang Deng",

year = "2008",

doi = "10.1109/MESA.2008.4735686",

language = "English",

isbn = "9781424423682",

series = "2008 IEEE/ASME International Conference on Mechatronics and Embedded Systems and Applications, MESA 2008",

pages = "353--356",

booktitle = "2008 IEEE/ASME International Conference on Mechatronics and Embedded Systems and Applications, MESA 2008",

note = "2008 IEEE/ASME International Conference on Mechatronics and Embedded Systems and Applications, MESA 2008 ; Conference date: 12-12-2008 Through 15-12-2008",

}

Ma, T, Chen, J, Chen, W & Deng, F 2008, Neural network based inverse control of systems with hysteresis. in 2008 IEEE/ASME International Conference on Mechatronics and Embedded Systems and Applications, MESA 2008., 4735686, 2008 IEEE/ASME International Conference on Mechatronics and Embedded Systems and Applications, MESA 2008, pp. 353-356, 2008 IEEE/ASME International Conference on Mechatronics and Embedded Systems and Applications, MESA 2008, Beijing, China, 12/12/08. https://doi.org/10.1109/MESA.2008.4735686

Neural network based inverse control of systems with hysteresis. / Ma, Tao; Chen, Jie; Chen, Wenjie et al.
2008 IEEE/ASME International Conference on Mechatronics and Embedded Systems and Applications, MESA 2008. 2008. p. 353-356 4735686 (2008 IEEE/ASME International Conference on Mechatronics and Embedded Systems and Applications, MESA 2008).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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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%.

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SN - 9781424423682

T3 - 2008 IEEE/ASME International Conference on Mechatronics and Embedded Systems and Applications, MESA 2008

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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 -

Neural network based inverse control of systems with hysteresis

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