Recursive zonotopic set-membership approach for system-level prognostics with application to linear parameter-varying systems

A robust recursive zonotopic set-membership approach for remaining useful life forecasting with application to linear parameter-varying systems is proposed in this paper. The proposed approach addresses systems with degraded components formulated as a system-level prognostics problem. Thus, the crit...

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Detalles Bibliográficos
Autores: Al Mohamad, Ahmad, Puig Cayuela, Vicenç|||0000-0002-6364-6429, Hoblos, Ghaleb
Tipo de recurso: artículo
Fecha de publicación:2022
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/384246
Acceso en línea:https://hdl.handle.net/2117/384246
https://dx.doi.org/10.1016/j.isatra.2022.09.031
Access Level:acceso abierto
Palabra clave:Predictive control
Prognostics
Zonotopes
Set-membership
Linear parameter-varying
Linear matrix inequality
Joint state-parameter estimation
Degraded systems
Control predictiu
Àrees temàtiques de la UPC::Informàtica::Automàtica i control
Descripción
Sumario:A robust recursive zonotopic set-membership approach for remaining useful life forecasting with application to linear parameter-varying systems is proposed in this paper. The proposed approach addresses systems with degraded components formulated as a system-level prognostics problem. Thus, the critical degraded components of the system are augmented to the states resulting a nonlinear system that is reformulated as a linear parameter-varying model. Hence, joint estimation of states and parameters is adopted in a zonotopic set-membership scheme with an optimal linear matrix inequality-based tuning and assuming unknown-but-bounded noises and uncertainties. As a result, a recursive zonotopic set-membership approach is proposed for remaining useful life forecasting based on the prediction of the failure precursors of degraded systems. Finally, this approach is tested on a DC–DC converter case study with unknown degradation behaviors, and the obtained results show the estimation and the forecasting accuracy of this methodology.