Fault detection for T–S fuzzy systems with unmeasurable premise variables based on a two-step interval estimation method
This paper proposes a fault detection strategy based on a two-step interval estimation method for T–S fuzzy systems with unmeasurable premise variables. First, an L_8 observer is designed to achieve robust point estimation under Lipschitz conditions. Then, the estimated error bounds are analyzed and...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2024 |
| 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:2117/427929 |
| Acceso en línea: | https://hdl.handle.net/2117/427929 https://dx.doi.org/10.1016/j.jprocont.2024.103341 |
| Access Level: | acceso embargado |
| Palabra clave: | Fault location (Engineering) Avaries -- Localització Àrees temàtiques de la UPC::Informàtica::Automàtica i control |
| Sumario: | This paper proposes a fault detection strategy based on a two-step interval estimation method for T–S fuzzy systems with unmeasurable premise variables. First, an L_8 observer is designed to achieve robust point estimation under Lipschitz conditions. Then, the estimated error bounds are analyzed and optimized using the L_8 performance conditions to enable interval estimation. Furthermore, the residual threshold is derived from the interval estimation to achieve robust fault detection. Finally, an activated sludge process in a wastewater treatment is considered to validate the proposed method. Simulation results demonstrate that the proposed approach can provide more accurate state interval estimation and outperforms standard L_8 observer design methods in addressing fault detection problems compared with existing methods. |
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