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Engineering - Control Engineering | Fault Detection and Flight Data Measurement - Demonstrated on Unmanned Air Vehicles using Neural

Fault Detection and Flight Data Measurement

Demonstrated on Unmanned Air Vehicles using Neural Networks

Samy, Ihab, Gu, Da-Wei

2011, XX, 176p. 82 illus., 23 illus. in color.

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  • Introduction to the popular topic of fault detection and isolation (FDI)
  • Design of flush air data sensing (FADS) and its application to a unmanned air vehicle
  • Written by a leading expert in the field

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.

Content Level » Research

Keywords » Control - Fault Detection and Isolation (FDI) - Flush Air Data Sensing (FADS) - Mini Air Vehicle-Motion - Neural Networks

Related subjects » Applications - Computational Intelligence and Complexity - Control Engineering - Mechanical Engineering

Table of contents 

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.

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