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Browsing by Author "Regan, Christopher"

Now showing 1 - 8 of 8
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    mAEWing2 Flight Data, HuginnFLT01
    (2019-12-30) Regan, Christopher
    Huginn Flight 01.
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    mAEWing2 Flight Data, HuginnFLT02
    (2019-12-30) Regan, Christopher
    mAEWing2, Huginn Flight 2.
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    mAEWing2 Flight Data, HuginnFLT03
    (2019-12-30) Regan, Christopher
    mAEWing2, Huginn Flight 3
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    mAEWing2 Flight Data, HuginnFLT04
    (2019-12-30) Regan, Christopher
    mAEWing2, Huginn Flight 04.
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    mAEWing2 Flight Data, HuginnFLT05
    (2019-12-30) Regan, Christopher
    mAEWing2, Huginn Flight 05
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    mAEWing2 Flight Data, HuginnFLT06
    (2019-12-30) Regan, Christopher
    mAEWing2, Huginn Flight 06
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    mAEWing2 Flight Data, HuginnFLT07
    (2019-12-30) Regan, Christopher
    mAEWing2, Huginn Flight 07
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    Non-Parametric Estimation of Uncertain Closed-Loop Multivariate Frequency Response for Stability Assurance
    (2022-03) Regan, Christopher
    System identification of closed-loop, multivariate systems presents a complex challenge; the use case for this study is on estimation for the purpose of stability margin assurance. Assessing both the nominal, or "best", estimated stability margins and the uncertainty range of those estimates are critical. This challenge is addressed by subjecting the system to multisine excitations and evaluating the response at both the excited frequencies and a set of null frequencies that are interleaved with the excited frequencies. This unique form of frequency separation allows for isolation of the response due to disturbances; which provides a critical source of estimation uncertainty. Additional sources of uncertainty, arising from response variations with time and spectral leakage, are combined to form a total estimated uncertainty. The impact of nonlinearities in the response are addressed, along with a particular approach to identification of nonlinearities. System stability assessment is performed, that directly accounts for estimation uncertainty. The approach to system estimation and stability assessment requires minimal prior knowledge and relies on only in situ data; the result is an independent assurance of system stability.