Integration of Online Parameter Identification and Neural Network for In-Flight Adaptive Control

Hageman, Jacob J.; Smith, Mark S.; Stachowiak, Susan J.

doi:10.2514/6.2003-5700

Cited by 25 publications

(23 citation statements)

References 7 publications

(10 reference statements)

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“…Adaptive functionality in safety-critical systems is already a reality. Applications of adaptive computing in safety critical systems are on the rise [39,53]. Autonomous software adaptation raises new challenges in the legal and social context.…”

Section: Adaptation-specific Model-driven Environmentsmentioning

confidence: 99%

Software Engineering for Self-Adaptive Systems: A Research Roadmap

Cheng¹,

Lemos²,

Giese³

et al. 2009

Lecture Notes in Computer Science

787

535

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Abstract. The goal of this roadmap paper is to summarize the state-ofthe-art and to identify critical challenges for the systematic software engineering of self-adaptive systems. The paper is partitioned into four parts, one for each of the identified essential views of self-adaptation: modelling dimensions, requirements, engineering, and assurances. For each view, we present the state-of-the-art and the challenges that our community must address. This roadmap paper is a result of the Dagstuhl Seminar 08031 on "Software Engineering for Self-Adaptive Systems, " which took place in January 2008.

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Section: Adaptation-specific Model-driven Environmentsmentioning

confidence: 99%

Software Engineering for Self-Adaptive Systems: A Research Roadmap

Cheng¹,

Lemos²,

Giese³

et al. 2009

Lecture Notes in Computer Science

787

535

View full text Add to dashboard Cite

show abstract

“…The first flight phase, referred to as "generation 1" ("Gen1"), focused on the development T and evaluation of an aerodynamic parameter identification method. 1 The second flight phase, referred to as "generation 2" ("Gen2"), focused on the development and evaluation of a direct adaptive control method that uses neural networks to generate command augmentation signals to compensate for errors caused by unmodeled dynamics. 2 In principle, this approach does not require information on the nature of the failure or the extent of the damage.…”

Section: Introduction and Statement Of The Problemmentioning

confidence: 99%

Flight Test Comparison of Different Adaptive Augmentations of Fault Tolerant Control Laws for a Modified F-15 Aircraft

Burken

Hanson

Lee

et al. 2009

AIAA Infotech@Aerospace Conference

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This report describes the improvements and enhancements to a neural network-based approach for directly adapting to aerodynamic changes resulting from damage or failures. This research is a follow-on effort to flight tests performed on the NASA F-15 aircraft as part of the Intelligent Flight Control System research effort. Previous flight test results demonstrated the potential for performance improvement under destabilizing damage conditions. Little or no improvement was provided under simulated control surface failures, however, and the adaptive system was prone to pilot-induced oscillations. An improved controller was designed to reduce the occurrence of pilot-induced oscillations and increase robustness to failures in general. This report presents an analysis of the neural networks used in the previous flight test, the improved adaptive controller, and the baseline case with no adaptation. Flight test results demonstrate significant improvement in performance by using the new adaptive controller compared with the previous adaptive system and the baseline system for control surface failures. Nomenclature

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“…2 For flight, the DCS algorithm was modified into five distinct networks to improve the speed and accuracy. 5 Various parameters were tuned to improve performance and were changeable by way of a configuration file that could be loaded prior to a particular flight. Inputs to the DCS include Mach, altitude, α, β, and surface positions, along with the derivative increments estimated by the PID and their respective validity flags.…”

Section: Dynamic Cell Structurementioning

confidence: 99%

Selected Flight Test Results for Online Learning Neural Network-Based Flight Control System

Williams-Hayes

2004

AIAA 1st Intelligent Systems Technical Conference

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Integration of Online Parameter Identification and Neural Network for In-Flight Adaptive Control

Cited by 25 publications

References 7 publications

Software Engineering for Self-Adaptive Systems: A Research Roadmap

Software Engineering for Self-Adaptive Systems: A Research Roadmap

Flight Test Comparison of Different Adaptive Augmentations of Fault Tolerant Control Laws for a Modified F-15 Aircraft

Selected Flight Test Results for Online Learning Neural Network-Based Flight Control System

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