Robust filtering for discrete-time systems with bounded noise andparametric uncertainty
El Ghaoui, L.
Calafiore, G.
Dept. of Electr. Eng. & Comput. Sci., California Univ., Berkeley, CA;
This paper appears in: Automatic Control, IEEE Transactions on
Publication Date: Jul 2001
Volume: 46,
Issue: 7
On page(s): 1084-1089
ISSN: 0018-9286
References Cited: 28
CODEN: IETAA9
INSPEC Accession Number: 6994784
Digital Object Identifier: 10.1109/9.935060
Current Version Published: 2002-08-07
Abstract
This note presents a new approach to finite-horizon guaranteed
state prediction for discrete-time systems affected by bounded noise and
unknown-but-bounded parameter uncertainty. Our framework handles
possibly nonlinear dependence of the state-space matrices on the
uncertain parameters. The main result is that a minimal confidence
ellipsoid for the state, consistent with the measured output and the
uncertainty description, may be recursively computed in polynomial time,
using interior-point methods for convex optimization. With n states, l
uncertain parameters appearing linearly in the state-space matrices,
with rank-one matrix coefficients, the worst-case complexity grows as
O(l(n + l)3.5) With unstructured uncertainty in all system
matrices, the worst-case complexity reduces to O(n3.5)
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