Identification of Precision Positioning Stage Based on ARX Model

Article Preview

Abstract:

In order to realize the characteristics of the positioning stage, the system identification has been developed. The location data has been interpolated and normalized so as to improve the accuracy of system identification. After data preprocessing, the identification based on ARX model and the least square method has been carried out. The results indicate that the ARX model is appropriate to describe the characteristics of positioning system.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

672-675

Citation:

Online since:

February 2013

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2013 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Devasia S, Eleftheriou E and Moheimani, SOR: A survey of control issues in nanopositioning, IEEE Transactions on Control Systems Technology Vol. 15 (2007), p.802.

DOI: 10.1109/tcst.2007.903345

Google Scholar

[2] Wahyudi, Kaiji Sato and Akira Shimokohbe: Characteristics of practical control for point-to-point (PTP) positioning systems effect of design parameters and actuator saturation on positioning performance, Precision Engineering Vol. 27 (2003).

DOI: 10.1016/s0141-6359(02)00226-x

Google Scholar

[3] Giorgos A, Spyros T: On-line RBFNN based identification of rapidly time-varying nonlinear systems with optimal structures-adaptation. Mathematics and Computers in Simulation Vol. 63 (2003), p.1.

DOI: 10.1016/s0378-4754(02)00159-3

Google Scholar

[4] Yu Kai-ping, Mou Xiao-ming: Improved EKF algorithms for nonlinear time-varying system identification based on feed forward neural network. Journal of Vibration and Shock Vol. 29 (2010), p.5.

Google Scholar

[5] Pan YP, Wang J: Model predictive control of unknown nonlinear dynamical systems based on recurrent neural networks. IEEE Transactions on Industrial Electronics Vol. 59 (2012), p.3089.

DOI: 10.1109/tie.2011.2169636

Google Scholar

[6] Zhang Hui-dong, Zhou Ying, Lian Ji-jian: A method based on ARMA model for modal parameter identification of a power house. Journal of Vibration and Shock Vol. 26 (2007), p.115.

Google Scholar

[7] Magalhaes RS, Fontes CHO, Almeida LAL: Identification of hybrid ARX-neural network models for three-dimensional simulation of a vibroacoustic system. Journal of Sound and Vibration Vol. 330 (2011), p.5138.

DOI: 10.1016/j.jsv.2011.05.024

Google Scholar

[8] Jin Hai-wei: Gearbox modeling based on ARX model. Journal of Vibration and Shock Vol. 30 (2011), p.230.

Google Scholar

[9] Kong Zhao-dan, Luo Wen-bo, He Rui: Research on black-box identification based on DBM method. Journal of System Simulation Vol. 19 (2007), p.2429.

Google Scholar

[10] Sun Lu, Wang Jiali, Chen Xiaolong: Two frequency domain black-box models of microwave nonlinear circuit based on measurement. Journal of Electronic Measurement and Instrument Vol. 23 (2009), p.19.

DOI: 10.3724/sp.j.1187.2009.10019

Google Scholar