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Data-driven robust receding horizon fault estimation
Delft University of Technology, Netherlands.
Delft University of Technology, Netherlands.
Delft University of Technology, Netherlands.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
2016 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 71, p. 210-221Article in journal (Refereed) Published
Abstract [en]

This paper presents a data-driven receding horizon fault estimation method for additive actuator and sensor faults in unknown linear time-invariant systems, with enhanced robustness to stochastic identification errors. State-of-the-art methods construct fault estimators with identified state-space models or Markov parameters, without compensating for identification errors. Motivated by this limitation, we first propose a receding horizon fault estimator parameterized by predictor Markov parameters. This estimator provides (asymptotically) unbiased fault estimates as long as the subsystem from faults to outputs has no unstable transmission zeros. When the identified Markov parameters are used to construct the above fault estimator, stochastic identification errors appear as model uncertainty multiplied with unknown fault signals and online system inputs/outputs (I/O). Based on this fault estimation error analysis, we formulate a mixed-norm problem for the offline robust design that regards online I/O data as unknown. An alternative online mixed-norm problem is also proposed that can further reduce estimation errors at the cost of increased computational burden. Based on a geometrical interpretation of the two proposed mixed-norm problems, systematic methods to tune the user-defined parameters therein are given to achieve desired performance trade-offs. Simulation examples illustrate the benefits of our proposed methods compared to recent literature. (C) 2016 Elsevier Ltd. All rights reserved.

Place, publisher, year, edition, pages
PERGAMON-ELSEVIER SCIENCE LTD , 2016. Vol. 71, p. 210-221
Keywords [en]
Data-driven methods; Fault estimation; Receding horizon estimation; Parameter uncertainty
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-131162DOI: 10.1016/j.automatica.2016.04.020ISI: 000381233700026OAI: oai:DiVA.org:liu-131162DiVA, id: diva2:972247
Note

Funding Agencies|European Unions Seventh Framework Programme (FP7-RECONFIGURE) [314544]

Available from: 2016-09-20 Created: 2016-09-12 Last updated: 2017-11-21

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Citation style
  • apa
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Language
  • de-DE
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