Least-squares-based iterative identification algorithm for Hammerstein nonlinear systems with non-uniform sampling

Abstract

This paper focuses on identification problems for Hammerstein systems with non-uniform sampling. By using the over-parameterization technique, we derive a linear regressive identification model with different input updating rates. To solve the identification problem of Hammerstein output error systems with the unmeasurable variables in the information vector, the least-squares-based iterative algorithm is presented by replacing the unmeasurable variables with their corresponding iterative estimates. The performances of the proposed algorithm are analysed and compared by using a numerical example.

Keywords:

• parameter estimation
• iterative algorithm
• auxiliary model identification
• least squares
• non-uniform sampling

2010 AMS Subject Classifications:

• 93B30
• 93A30

Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 61273194), the Natural Science Foundation of Jiangsu Province (China, BK2012549), the University Graduate Scientific Research Innovation Program of Jiangsu Province (CXLX12_0722), the PhD Candidate Scientific Research Foundation of Jiangnan University (JUDCF11042) and the 111 Project (B12018).