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Distributed 3D Navigation and Collision Avoidance Nonholonomic Aircraft-like Vehicles
NTUA.
NTUA.
2009 (English)In: Proceedings of 2009 European Control Conference, 2009Conference paper, Published paper (Refereed)
Abstract [en]

Algorithms for robust failure or fault detection and identification (RFDI) in dynamic plants and control systems strive to be sensitive to the failures in plant components, while being insensitive to plant, failure and noise model uncertainties. In signal detection, sensitivity to signals of interest is also an objective, while robustness is concerned with undesirable interferences residing in unknown or partially known subspaces, and uncertainties in the signal and learned interference models. We will present the similarities and differences in the game theoretic formulations and solution approaches to robust detection in control systems and in signals. We will also show how ideas from RFDI in dynamic systems provide insight into robust detection in signals, and vice versa. We will discuss how the resulting innovative formulations and approaches to robust detection, estimation and learning are successfully addressing challenging problems in applications ranging from network flow to functional magnetic resonance imaging (fMRI) of the brain, to non-invasive image based classification of stem cells

Place, publisher, year, edition, pages
2009.
Keyword [en]
Fault detection and identification, Fault estimation, Signal processing
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-105823OAI: oai:DiVA.org:kth-105823DiVA: diva2:572363
Conference
2009 European Control Conference
Note

QC 20130528

Available from: 2012-11-27 Created: 2012-11-27 Last updated: 2013-05-28Bibliographically approved

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Dimarogonas, Dimos V.

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  • apa
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Output format
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