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Nonlinear Dynamics

, Volume 75, Issue 4, pp 709–716 | Cite as

Iterative estimation methods for Hammerstein controlled autoregressive moving average systems based on the key-term separation principle

  • Qianyan Shen
  • Feng Ding
Original Paper

Abstract

This paper considers iterative identification problems for a Hammerstein nonlinear system which consists of a memoryless nonlinear block followed by a linear dynamical block. The difficulty of identification is that the Hammerstein nonlinear system contains the products of the parameters of the nonlinear part and the linear part, which leads to the unidentifiability of the parameters. In order to obtain unique parameter estimates, we express the output of the system as a linear combination of all the system parameters by means of the key-term separation principle and derive a gradient based iterative identification algorithm by replacing the unknown variables in the information vectors with their estimates. The simulation results indicate that the proposed algorithm can work well.

Keywords

Iterative algorithm Parameter estimation Recursive identification Gradient search Hammerstein system Key-term separation principle 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 61273194), the Natural Science Foundation of Jiangsu Province (China, BK2012549), and the PAPD of Jiangsu Higher Education Institutions.

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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education)Jiangnan UniversityWuxiP.R. China
  2. 2.Control Science and Engineering Research CenterJiangnan UniversityWuxiP.R. China

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