Identification of High-Order Linear Time-Invariant Models from Periodic Nonlinear System Responses

This paper presents a novel step in the extension of subspace identification toward the direct identification of harmonic decomposition linear time-invariant models from nonlinear time-periodic system responses. The proposed methodology is demonstrated through examples involving the nonlinear time-p...

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Bibliographic Details
Published in:Aerospace 2024-11, Vol.11 (11), p.875
Main Authors: Hayajnh, Mahmoud A., Saetti, Umberto, Prasad, J. V. R.
Format: Article
Language:English
Subjects:
Algorithms
Decomposition
Dynamic structural analysis
Dynamical systems
flapping wing
Flapping wings
Identification
Invariants
linear time-periodic systems
Methods
micro aerial vehicle
Micro air vehicles (MAV)
Model matching
Model reduction
Noise measurement
Nonlinear dynamics
Nonlinear systems
nonlinear time-periodic systems
rotorcraft
system identification
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Summary:This paper presents a novel step in the extension of subspace identification toward the direct identification of harmonic decomposition linear time-invariant models from nonlinear time-periodic system responses. The proposed methodology is demonstrated through examples involving the nonlinear time-periodic dynamics of a flapping-wing micro aerial vehicle. These examples focus on the identification of the vertical dynamics from various types of input–output data, including linear time-invariant, linear time-periodic, and nonlinear time-periodic input–output data. A harmonic analyzer is used to decompose the linear time-periodic and nonlinear time-periodic responses into harmonic components and introduce spurious dynamics into the identification, which make the identified model order selection challenging. A similar effect is introduced by measurement noise. The use of model order reduction and model-matching methods in the identification process is studied to recover the harmonic decomposition structure of the known system. The identified models are validated in the frequency and time domains.
ISSN:2226-4310
2226-4310
DOI:10.3390/aerospace11110875