Affiliation:
1. University of Science and Technology of China
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.
Publisher
Trans Tech Publications, Ltd.
Reference10 articles.
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.
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).
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.
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.
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.