State monitoring of systems augmented by model reference adaptive control using analytic time-series forecasting

Maximilian Mühlegg, Girish Chowdhary, Florian Holzapfel

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Certification of adaptive control algorithms for use on aerospace applications has not yet been accomplished in the aerospace industry. According to an emerging consensus between various authors, online monitoring and health assessment will play an integral role in closing this gap. In this paper we propose a monitoring system for Model Reference Adaptive Controllers, which enables online detection of future state requirement violations. We achieve this by employing Gaussian Process regression, which leads to a belief on the uncertainty in the system dynamics. Using analytic time-series forecasting, the system dynamics can be projected into the future, thus allowing for a statistical assertion whether a state requirement will be violated during the prediction horizon. We show the concept in numerical simulation.

Original languageEnglish (US)
Title of host publication24th Mediterranean Conference on Control and Automation, MED 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages437-442
Number of pages6
ISBN (Electronic)9781467383455
DOIs
StatePublished - Aug 5 2016
Externally publishedYes
Event24th Mediterranean Conference on Control and Automation, MED 2016 - Athens, Greece
Duration: Jun 21 2016Jun 24 2016

Publication series

Name24th Mediterranean Conference on Control and Automation, MED 2016

Other

Other24th Mediterranean Conference on Control and Automation, MED 2016
CountryGreece
CityAthens
Period6/21/166/24/16

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Control and Optimization
  • Modeling and Simulation

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