Filtros de Kalman robustos para sistemas dinâmicos singulares em tempo discreto / Robust Kalman filters for discrete-time singular systems

AUTOR(ES)

Aline Fernanda Bianco

DATA DE PUBLICAÇÃO

2009

RESUMO

This thesis considers the optimal robust estimates problem for discrete-time singular dymanic systems. New recursive algorithms are developed for the Kalman filtered and predicted estimated recursions with the corresponding Riccati equations. The singular robust Kalman type filter and the corresponding recursive Riccati equation arer obtained in their most general formulation, extending the results presented in the literature. The quadratic functional developed to deduce this filter combines regularized least squares and penalty functions approaches. The system considered to obtain the estimates is singular, time varying with correlated noises and all parameter matrices of the underlying linear model are subject to uncertainties. The parametric uncertainty is assumed to be norm bounded. The properties of stability and convergence of the Kalman filter for nominal and uncertain system models are proved, where we show that steady state filter is stable and the Riccati recursion associated with this is a nondecreasing monotone sequence with upper bound.

ASSUNTO(S)

state estimation sistemas singulares convergência estabilidade robust filtering singular systems filtragem robusta convergence estimativa de estado stability

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