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Diagnosis of Hybrid Systems Using Hybrid Particle Petri Nets: Theory and Application on a Planetary Rover

  • Quentin Gaudel
  • Elodie Chanthery
  • Pauline Ribot
  • Matthew J. Daigle
Chapter

Abstract

This chapter presents a new methodology to perform health monitoring of hybrid systems under uncertainty. Hybrid systems can be represented as multi-mode systems with hybrid automata. Diagnosers are generated from these hybrid automata using a new data structure in order to monitor both the behavior and degradation of such systems. After a review of the state of the art on different existing solutions for diagnosis of hybrid systems under uncertainty, we propose to introduce the Hybrid Particle Petri Nets (HPPN) modeling framework. The main advantage of HPPN is that they take into account knowledge-based uncertainty in the system representation and uncertainty in the diagnosis process. The HPPN-based diagnoser deals with occurrences of unobservable discrete events (such as fault events) and it is robust to false observations. It also estimates the continuous state of the system by using particle filtering. A methodology is proposed to perform model-based diagnosis on hybrid systems by using the HPPN modeling framework. The system diagnosis is computed at any time from a HPPN-based diagnoser and contains all the hypotheses over its past mode trajectory. Each hypothesis is valued with a belief degree and includes discrete and continuous state estimates, as well as the set of faults that occurred on the system up to the current time. The HPPN-based methodology is demonstrated with an application on the K11 planetary rover prototype developed by NASA Ames Research Center. A hybrid model of the K11 is proposed and experimental results show that the approach is robust to real system data and constraints.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Quentin Gaudel
    • 1
  • Elodie Chanthery
    • 2
  • Pauline Ribot
    • 2
  • Matthew J. Daigle
    • 3
  1. 1.EasyMile SASToulouseFrance
  2. 2.LAAS-CNRSUniversité de Toulouse, CNRS, INSA, UPSToulouseFrance
  3. 3.NIO USA, Inc.San JoseUSA

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