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Mode Estimation of Probabilistic Hybrid Systems

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Hybrid Systems: Computation and Control (HSCC 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2289))

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Abstract

Model-based diagnosis and mode estimation capabilities excel at diagnosing systems whose symptoms are clearly distinguished from normal behavior. A strength of mode estimation, in particular, is its ability to track a system’s discrete dynamics as it moves between different behavioral modes. However, often failures bury their symptoms amongst the signal noise, until their effects become catastrophic. p] We introduce a hybrid mode estimation system that extracts mode estimates from subtle symptoms. First, we introduce a modeling formalism, called concurrent probabilistic hybrid automata (cPHA), that merge hidden Markov models (HMM) with continuous dynamical system models. Second, we introduce hybrid estimation as a method for tracking and diagnosing cPHA, by unifying traditional continuous state observers with HMM belief update. Finally, we introduce a novel, any-time, any-space algorithm for computing approximate hybrid estimates.

Supported by NASA under contract NAG2-1388.

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Author information

Authors and Affiliations

  1. MIT Space Systems and Artificial Intelligence Laboratories, 77 Massachusetts Ave., Rm-37-381, 02139, Cambridge, MA, USA

    Michael W. Hofbaur & Brian C. Williams

  2. Department of Automatic Control, Graz University of Technology, Infieldgasse 16c/2, A-8010, Graz, Austria

    Michael W. Hofbaur

Authors
  1. Michael W. Hofbaur
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  2. Brian C. Williams
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Editor information

Editors and Affiliations

  1. Department of Aeronautics and Astronautics, Stanford University, 250 Durand Building MC4035, 94305-4035, Stanford, CA, USA

    Claire J. Tomlin

  2. Department of Computer Science, University of British Columbia, 201-2366 Main Mall, V6T1Z4, Vancouver BC, Canada

    Mark R. Greenstreet

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© 2002 Springer-Verlag Berlin Heidelberg

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Hofbaur, M.W., Williams, B.C. (2002). Mode Estimation of Probabilistic Hybrid Systems. In: Tomlin, C.J., Greenstreet, M.R. (eds) Hybrid Systems: Computation and Control. HSCC 2002. Lecture Notes in Computer Science, vol 2289. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45873-5_21

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  • DOI: https://doi.org/10.1007/3-540-45873-5_21

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43321-7

  • Online ISBN: 978-3-540-45873-9

  • eBook Packages: Springer Book Archive

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