Unknown configuration autopilot platform design using machine learning / by Adham Esmat Abdelaziz Mousa ; Supervision of Prof. Dr. Gamal Mahmoud El-Bayomi, Prof. Dr. Osama Saaid Mohamady.

By:

Adham Esmat Abdelaziz Mousa [preparation.]

Contributor(s):

Material type: Text

TextLanguage: English Summary language: English, Arabic Producer: 2024Description: 172 pages : illustrations ; 30 cm. + CDContent type:

text

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Unmediated

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volume

Other title:

تصميم منظومة طيار آلى للطائرات غير محددة التكوين باستخدام تعلم الآلة [Added title page title]

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DDC classification:

005.31

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Issues also as CD.

Dissertation note: Thesis (M.Sc)-Cairo University, 2024. Summary: One of the ways to realize unknown configuration autopilot is the usage of system identification to estimate the aircraft parameters based on the variation of A/C configuration and flight conditions. The system identification needs an initial condition rather than using random values to find the parameters, to avoid the probability of converging to the wrong set of parameters. Our approach is the usage of machine learning to identify the initial conditions based on aircraft data. Also, the approach discussed in this thesis acts as a basis for real-time system identification where the aircraft model becomes more accurate by feeding real-time data during the flight. This thesis implements a machine learning life cycle on a dataset which is built from scratch as part of the thesis contribution

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Item type	Current library	Home library	Call number	Status	Barcode
Thesis	قاعة الرسائل الجامعية - الدور الاول	المكتبة المركزبة الجديدة - جامعة القاهرة	Cai01.13.01.M.Sc.2024.Ad.U (Browse shelf(Opens below))	Not for loan	01010110090690000

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Previous	Cai01.13.01.M.Sc.2023.Lu.N Numerical Investigation of the dynamic performance of a supersonic intake with variable geometry/ Remainder of title /	Cai01.13.01.M.Sc.2023.Mo.E effect of heaving motion on the performance of an airfoil at low reynolds numbers for micro-air-vehicles/	Cai01.13.01.M.Sc.2023.Mo.N Numerical Simulation of Flow-Induced Vibration of Two Cylinders in Tandem Using Level Set Method /	Cai01.13.01.M.Sc.2024.Ad.U Unknown configuration autopilot platform design using machine learning /	Cai01.13.01.M.Sc.2024.Ah.N Nonlinear system identification with experimental : case study /	Cai01.13.01.M.Sc.2024.Ah.O Optimum structural design of a small composite satellite /	Cai01.13.01.M.Sc.2024.Ge.M Analytical and experimental improvement of flutter performance of plate wings using piezoelectric patches /	Next

Thesis (M.Sc)-Cairo University, 2024.

Bibliography: pages 80

One of the ways to realize unknown configuration autopilot is the usage of system identification to estimate the aircraft parameters based on the variation of A/C configuration and flight conditions. The system identification needs an initial condition rather than using random values to find the parameters, to avoid the probability of converging to the wrong set of parameters.
Our approach is the usage of machine learning to identify the initial conditions based on aircraft data. Also, the approach discussed in this thesis acts as a basis for real-time system identification where the aircraft model becomes more accurate by feeding real-time data during the flight. This thesis implements a machine learning life cycle on a dataset which is built from scratch as part of the thesis contribution

Issues also as CD.

Text in English and abstract in Arabic & English.

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