Approximation of the Feasible Parameter Set in worst-case identification of Hammerstein models

Automatica

Volumn 41, Issue 6, 2005, Pages 1017-1024

  Falugi, P ^a   Giarre L ^b   Zappa, G ^a  

^a UNIVERSITY OF FLORENCE   (Italy)

^b Dipto Di Ingegneria dell'A   (Italy)

Author keywords

Hammerstein models; Nonlinear systems identification; Set membership; Uncertainty intervals

Indexed keywords

APPROXIMATION THEORY; FIR FILTERS; IDENTIFICATION (CONTROL SYSTEMS); LINEARIZATION; MATHEMATICAL MODELS; PARAMETER ESTIMATION; POLYNOMIALS; SET THEORY; VECTORS;

HAMMERSTEIN MODELS; NONLINEAR SYSTEM IDENTIFICATION; SET MEMBERSHIP; UNCERTAINTY INTERVALS;

NONLINEAR SYSTEMS;

EID: 17644418363     PISSN: 00051098     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.automatica.2004.12.010     Document Type: Article

References (8)

1. E.-W. Bai An optimal two-stage identification algorithm for Hammerstein-Wiener nonlinear systems Automatica 34 3 1998 333 338
2. G. Belforte, and P. Gay Discrete time Hammerstein model identification with unknown but bounded errors IEE Proceedings of the control theory and applications 148 2001 523 529
3. H. Chen Modeling and identification of parallel nonlinear systems Structural classification and parameter estimation models Proceedings of IEEE 83 1985 39 66
4. A. Garulli, L. Giarrè, and G. Zappa Identification of approximated Hammerstein models in a worst case setting IEEE Transactions on Automatic Control 47 12 2002 2046 2050
5. J.C. Gómez, and E. Baeyens Identification of block-oriented nonlinear systems using orthonormal bases Journal of Process Control 14 2004 685 697
6. R. Keerthi, and E.G. Gilbert Computation of minimum-time feedback control laws for discrete-time systems with state-control constraints IEEE Transactions on Automatic Control 32 1987 432 435
7. K.S. Narendra, and P.G. Gallman An iterative method for the identification of nonlinear systems using a Hammerstein model IEEE Transactions on Automatic Control 11 1966 546 550
8. R. Pearson, and M. Pottmann Gray-box identification of block oriented nonlinear models Journal of Process Control 10 2000 301 315