Real-time Fault Estimation for a Class of Discrete-Time Linear Parameter-Varying Systems

None, None; None, None; None, None; None, None

Real-time Fault Estimation for a Class of Discrete-Time Linear Parameter-Varying Systems

Journal Article (2022)

Author(s)

Chris van der Ploeg (Eindhoven University of Technology, TNO)

Emilia Silvas (Eindhoven University of Technology, TNO)

Nathan van de Wouw (University of Minnesota, Eindhoven University of Technology)

Peyman Mohajerin Esfahani (TU Delft - Mechanical Engineering, TU Delft - Mechanical Engineering)

Research Group

Team Peyman Mohajerin Esfahani

Estimation Fault diagnosis Vehicle dynamics Fault detection Real-time systems Fault estimation Mathematical models Linear parameter-varying systems. Time-varying systems Q measurement

DOI related publication

https://doi.org/10.1109/LCSYS.2021.3137711 Final published version

To reference this document use

https://resolver.tudelft.nl/uuid:741ba82a-8264-47b0-82ef-041c771a22a2

More Info

expand_more

Publication Year

2022

Language

English

Research Group

Team Peyman Mohajerin Esfahani

Bibliographical Note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Journal title

IEEE Control Systems Letters

Volume number

6

Pages (from-to)

1988-1993

Downloads counter

358

Collections

Institutional Repository

Abstract

Estimating and detecting faults is crucial in ensuring safe and efficient automated systems. In the presence of disturbances, noise, or varying system dynamics, such estimation is even more challenging. To address this challenge, this letter proposes a novel filter to estimate multiple fault signals for a class of discrete-time linear parameter-varying (LPV) systems. The design of such a filter is formulated as an optimization problem and is solved recursively, while the system dynamics may vary over time. Conditions for the existence and detectability of the fault are introduced and the problem is formulated and solved using the quadratic programming framework. We further propose an approximate scheme that can be arbitrarily precise while it enjoys an analytical solution, which supports real-time implementation. The method is illustrated and validated on an automated vehicle's lateral dynamics, which is a practically relevant example for LPV systems. The results show that the estimation filter can decouple unknown disturbances and known or measurable parameter variations in the dynamics while estimating the unknown fault.