Fault Detection and Flight Data Measurement [recurso electrónico] : Demonstrated on Unmanned Air Vehicles Using Neural Networks / by Ihab Samy, Da-Wei Gu.

Por: Samy, Ihab [author.]Colaborador(es): Gu, Da-Wei [author.] | SpringerLink (Online service)Tipo de material: TextoTextoSeries Lecture Notes in Control and Information Sciences ; 419Editor: Berlin, Heidelberg : Springer Berlin Heidelberg, 2011Descripción: XX, 176p. 82 illus., 23 illus. in color. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9783642240522Tema(s): Engineering | Systems theory | Astronautics | Engineering | Control | Computational Intelligence | Aerospace Technology and Astronautics | Systems Theory, ControlFormatos físicos adicionales: Printed edition:: Sin títuloClasificación CDD: 629.8 Clasificación LoC:TJ212-225Recursos en línea: Libro electrónicoTexto
Contenidos:
Introduction -- Fault detection and isolation (FDI) -- Introduction to FADS systems -- Neural Networks -- SFDA-Single sensor faults -- SFDIA-Multiple sensor faults -- FADS system applied to a MAV -- Conclusions and Future Work.
En: Springer eBooksResumen: This book considers two popular topics: fault detection and isolation (FDI) and flight data estimation using flush air data sensing (FADS) systems. Literature surveys, comparison tests, simulations and wind tunnel tests are performed. In both cases, a UAV platform is considered for demonstration purposes. In the first part of the book, FDI is considered for sensor faults where a neural network approach is implemented. FDI is applied both in academia and industry resulting in many publications over the past 50 years or so. However few publications consider neural networks in comparison to traditional techniques such as observer based, parameter estimations and parity space approaches. The second part of this book focuses on how to estimate flight data (angle of attack, airspeed) using a matrix of pressure sensors and a neural network model. In conclusion this book can serve as an introduction to FDI and FADS systems, a literature survey, and a case study for UAV applications.
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Tipo de ítem Biblioteca actual Colección Signatura Copia número Estado Fecha de vencimiento Código de barras
Libro Electrónico Biblioteca Electrónica
Colección de Libros Electrónicos TJ212 -225 (Browse shelf(Abre debajo)) 1 No para préstamo 376739-2001

Introduction -- Fault detection and isolation (FDI) -- Introduction to FADS systems -- Neural Networks -- SFDA-Single sensor faults -- SFDIA-Multiple sensor faults -- FADS system applied to a MAV -- Conclusions and Future Work.

This book considers two popular topics: fault detection and isolation (FDI) and flight data estimation using flush air data sensing (FADS) systems. Literature surveys, comparison tests, simulations and wind tunnel tests are performed. In both cases, a UAV platform is considered for demonstration purposes. In the first part of the book, FDI is considered for sensor faults where a neural network approach is implemented. FDI is applied both in academia and industry resulting in many publications over the past 50 years or so. However few publications consider neural networks in comparison to traditional techniques such as observer based, parameter estimations and parity space approaches. The second part of this book focuses on how to estimate flight data (angle of attack, airspeed) using a matrix of pressure sensors and a neural network model. In conclusion this book can serve as an introduction to FDI and FADS systems, a literature survey, and a case study for UAV applications.

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