Title :
Frequency domain identification of Hammerstein models
Author_Institution :
Dept. of Electr. & Comput. Eng., Iowa Univ., Iowa City, IA, USA
fDate :
4/1/2003 12:00:00 AM
Abstract :
Discusses Hammerstein model identification in the frequency domain using sampled input-output data. By exploring the fundamental frequency and harmonics generated by the unknown nonlinearity, we propose a frequency domain approach and show its convergence for both the linear and nonlinear subsystems in the presence of noise. No a priori knowledge of the structure of the nonlinearity is required and the linear part can be nonparametric.
Keywords :
continuous time systems; curve fitting; frequency response; identification; nonlinear systems; nonparametric statistics; sampled data systems; Hammerstein models; convergence; frequency domain identification; fundamental frequency; harmonics; linear subsystems; noise; nonlinear subsystems; sampled input-output data; Convergence; Frequency domain analysis; Helium; Iterative methods; Least squares methods; Linear systems; Noise generators; Nonlinear systems; Parameter estimation; System identification;
Journal_Title :
Automatic Control, IEEE Transactions on
DOI :
10.1109/TAC.2003.809803
