NDLI: A Convex Optimization Approach to Synthesizing Bounded Complexity <formula formulatype=inline><tex Notation=TeX>$ell^{infty}$</tex> </formula> Filters

Content Provider	IEEE Xplore Digital Library
Author	Blanchini, F. Sznaier, M.
Copyright Year	1963
Abstract	We consider the worst-case estimation problem in the presence of unknown but bounded noise. Contrary to stochastic approaches, the goal here is to confine the estimation error within a bounded set. Previous work dealing with the problem has shown that the complexity of estimators based upon the idea of constructing the state consistency set (e.g., the set of all states consistent with the a priori information and experimental data) cannot be bounded a priori, and can, in principle, continuously increase with time. To avoid this difficulty we propose a class of bounded complexity filters, based upon the idea of confining r-length error sequences (rather than states) to hyperrectangles. The main result of the technical note shows that this can be accomplished by using linear time invariant filters of order no larger than r. Further, synthesizing these filters reduces to a combination of convex optimization and line search.
Sponsorship	IEEE Control Systems Society
Starting Page	216
Ending Page	221
Page Count	6
File Size	425912
File Format	PDF
ISSN	00189286
Volume Number	57
Issue Number	1
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Complexity theory Estimation error Observers Switches Noise Optimized production technology worst-case estimation Linear time invariant (LTI) $\ell^{\infty}$ filtering
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Electrical and Electronic Engineering Computer Science Applications

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