Auxiliary model based recursive generalized least squares parameter estimation for Hammerstein OEAR systems

Authors:
Dongqing Wang;Yanyun Chu;Guowei Yang;Feng Ding
Affiliations:
College of Automation Engineering, Qingdao University, Qingdao 266071, PR China;College of Automation Engineering, Qingdao University, Qingdao 266071, PR China;College of Automation Engineering, Qingdao University, Qingdao 266071, PR China;School of Communication and Control Engineering, Jiangnan University, Wuxi 214122, PR China
Venue:
Mathematical and Computer Modelling: An International Journal
Year:
2010

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Abstract

This paper deals with the parameter identification problem of Hammerstein output error auto-regressive (OEAR) systems with different nonlinearities by combining the key-term separation principle and the auxiliary model identification idea. The basic idea is, by using the key-term separation principle, to present auxiliary model based recursive generalized least squares algorithms in terms of the auxiliary model idea. The proposed algorithm can obtain the system model parameter estimates and the noise model parameter estimates, and can be extended to other nonlinear systems.