Please use this identifier to cite or link to this item: http://hdl.handle.net/11144/3428
Title: Fault detection for LPV systems using Set-Valued Observers: A coprime factorization approach
Authors: Silvestre, Daniel
Rosa, P.
Hespanha, J.P.
Silvestre, C.
Keywords: Fault Detection
Linear Parameter-Varying
Coprime Factorization
Deadbeat Observers
Issue Date: Aug-2017
Publisher: Elsevier
Citation: Daniel Silvestre, Paulo Rosa, João P. Hespanha, Carlos Silvestre, Fault detection for LPV systems using Set-Valued Observers: A coprime factorization approach, Systems & Control Letters, Volume 106, 2017, Pages 32-39, ISSN 0167-6911, https://doi.org/10.1016/j.sysconle.2017.05.007.
Abstract: This paper addresses the problem of fault detection for linear parameter-varying systems in the presence of measurement noise and exogenous disturbances. The applicability of current methods is limited in the sense that, to increase accuracy, the detection requires a large number of past measurements and the boundedness of the set-valued estimates is only guaranteed for stable systems. In order to widen the class of systems to be modeled and also to reduce the associated computational cost, the aforementioned issues must be addressed. A solution involving left-coprime factorization and deadbeat observers is proposed in order to reduce the required number of past measurements without compromising accuracy and allowing the design of Set-Valued Observers (SVOs) for fault detection of unstable systems by using the resulting stable subsystems of the coprime factorization. The algorithm is shown to produce bounded set-valued estimates and an example is provided. Performance is assessed through simulations, illustrating, in particular that small-magnitude faults (compared to exogenous disturbances) can be detected under mild assumptions.
Peer reviewed: yes
URI: http://hdl.handle.net/11144/3428
metadata.dc.identifier.doi: 10.1016/j.sysconle.2017.05.007
ISSN: 0167-6911
Publisher version: Elsevier
Appears in Collections:AUTONOMA TECHLAB - Artigos/Papers
DCT - Artigos/Papers

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