Title :
H∞ identification of “soft” uncertainty models
Author :
Milanese, M. ; Taragna, M.
Author_Institution :
Dipartimento di Autom. e Inf., Politecnico di Torino, Italy
Abstract :
The paper investigates the problem of identifying uncertainty models of SISO, LTI, discrete-time, BIBO stable, unknown systems, using frequency domain measurements corrupted by Gaussian noise of known covariance. An additive uncertainty model is looked for, consisting of a nominal model and an additive dynamic perturbation accounting for the modeling errors. The nominal model is chosen within a class of linearly parametrized models with transfer function of given (possibly low) order. An estimate of the parameters minimizing the H∞ modeling error is obtained by minimizing an upper bound of the worst case (with respect to modeling error) second moment of the estimation error. Then, a bound in the frequency domain guaranteeing to include, with probability α, the frequency response error between the estimated nominal model and the unknown system is derived
Keywords :
Banach spaces; Gaussian noise; discrete time systems; linear systems; parameter estimation; robust control; transfer functions; uncertain systems; Gaussian noise; H∞ identification; H∞ modeling error; LTI discrete-time BIBO stable unknown systems; SISO systems; additive dynamic perturbation; additive uncertainty model; estimation error; frequency domain bound; frequency domain measurements; frequency response error; linearly parametrized models; modeling errors; soft uncertainty models; upper bound; worst case second moment; Estimation error; Frequency domain analysis; Frequency estimation; Frequency measurement; Frequency response; Gaussian noise; Noise measurement; Parameter estimation; Transfer functions; Upper bound;
Conference_Titel :
Decision and Control, 1996., Proceedings of the 35th IEEE Conference on
Conference_Location :
Kobe
Print_ISBN :
0-7803-3590-2
DOI :
10.1109/CDC.1996.573451
