Mode Estimation of Probabilistic Hybrid Systems

  • Michael W. Hofbaur
  • Brian C. Williams
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2289)


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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Michael W. Hofbaur
    • 1
    • 2
  • Brian C. Williams
    • 1
  1. 1.MIT Space Systems and Artificial Intelligence LaboratoriesCambridgeUSA
  2. 2.Department of Automatic ControlGraz University of TechnologyGrazAustria

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