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Stochastic non sequitur behavior analysis of fault tolerant hybrid systems

Published: 12 April 2011 Publication History

Abstract

In this paper, we introduce a new stochastic analysis method for the uncontrollable behaviour of hybrid systems triggered by faults. Models like manoeuvre automata can be used for controllable transitions between stable modes. However, the normal behaviour can become easily unpredictable when a fault occurs, and a human or an automated supervisor needs to take very quickly the right actions to drive the system back into a controllable state. The system behaviour while transiting two controllable states is called non sequitur. Using stochastic analysis we investigate how to extract information about non sequitur that can be used in stochastic control.

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  1. Stochastic non sequitur behavior analysis of fault tolerant hybrid systems

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    cover image ACM Conferences
    HSCC '11: Proceedings of the 14th international conference on Hybrid systems: computation and control
    April 2011
    330 pages
    ISBN:9781450306294
    DOI:10.1145/1967701
    • General Chair:
    • Marco Caccamo,
    • Program Chairs:
    • Emilio Frazzoli,
    • Radu Grosu
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 12 April 2011

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    Author Tags

    1. fault tolerant control
    2. manoeuvre automata
    3. markov models
    4. non-sequitur behaviour
    5. stochastic hybrid systems

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    HSCC '11
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    HSCC '11: Hybrid Systems: Computation and Control
    April 12 - 14, 2011
    IL, Chicago, USA

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    Overall Acceptance Rate 153 of 373 submissions, 41%

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