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NASA-LLIS-1525

Lessons Learned - Problems Associated With the Use of a Dynamic Inversion Controller in Conjunction With an Adaptive Neural Network in the Presence of Surface Failures

Organization:
NASA - National Aeronautics and Space Administration (NASA)
Year: 2005

Abstract: Abstract:
The NASA F-15 Intelligent Flight Control System (IFCS) project team has developed a series of flight control concepts designed to demonstrate the benefits of a neural network-based adaptive controller. The objective of the team is to develop and flight-test control systems that use neural network technology to optimize the performance of the aircraft under nominal conditions as well as stabilize the aircraft under failure conditions. Failure conditions include locked or failed control surfaces as well as unforeseen damage that might occur to the aircraft in flight. The IFCS team is currently in the process of implementing a second generation control scheme, collectively known as Gen 2, for flight testing on the NASA F-15 aircraft.
The research controller is a dynamic inverse controller based on the Versatile Control Augmentation System (VCAS) control laws developed by James Buckley at McDonnell Douglas Aircraft. The baseline control laws were taken from the F-15 ACTIVE ILTV program, but the IFCS team has removed the thrust vectoring controls for the Gen 2 implementation. For the original dynamic inverse controller, the proportional, integral, & derivative (PID) gains were tuned to achieve linear stability robustness, ASE mode attenuation, and non-linear system command following for the nominal (no failure) case.
URI: https://yse.yabesh.ir/std/handle/yse/133287
Subject: Aircraft
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  • NASA - National Aeronautics and Space Administration (NASA)
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    NASA-LLIS-1525

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contributor authorNASA - National Aeronautics and Space Administration (NASA)
date accessioned2017-09-04T17:09:48Z
date available2017-09-04T17:09:48Z
date copyright02/02/2005
date issued2005
identifier otherYNWEQCAAAAAAAAAA.pdf
identifier urihttps://yse.yabesh.ir/std/handle/yse/133287
description abstractAbstract:
The NASA F-15 Intelligent Flight Control System (IFCS) project team has developed a series of flight control concepts designed to demonstrate the benefits of a neural network-based adaptive controller. The objective of the team is to develop and flight-test control systems that use neural network technology to optimize the performance of the aircraft under nominal conditions as well as stabilize the aircraft under failure conditions. Failure conditions include locked or failed control surfaces as well as unforeseen damage that might occur to the aircraft in flight. The IFCS team is currently in the process of implementing a second generation control scheme, collectively known as Gen 2, for flight testing on the NASA F-15 aircraft.
The research controller is a dynamic inverse controller based on the Versatile Control Augmentation System (VCAS) control laws developed by James Buckley at McDonnell Douglas Aircraft. The baseline control laws were taken from the F-15 ACTIVE ILTV program, but the IFCS team has removed the thrust vectoring controls for the Gen 2 implementation. For the original dynamic inverse controller, the proportional, integral, & derivative (PID) gains were tuned to achieve linear stability robustness, ASE mode attenuation, and non-linear system command following for the nominal (no failure) case.
languageEnglish
titleNASA-LLIS-1525num
titleLessons Learned - Problems Associated With the Use of a Dynamic Inversion Controller in Conjunction With an Adaptive Neural Network in the Presence of Surface Failuresen
typestandard
page3
statusActive
treeNASA - National Aeronautics and Space Administration (NASA):;2005
contenttypefulltext
subject keywordsAircraft
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DSpace software copyright © 2017-2020  DuraSpace
نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
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