Benefits of OKID in system realization with low amount of samples

Wagner, Gustavo; Lima, Roberta; Sampaio, Rubens

doi:10.1007/s42417-022-00671-0

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…The application emphasizes the estimation of the impulse response function using the Observer/Kalman Filter Identification (OKID) and uses the eigensystem realization algorithm (ERA) for the modal identification, although other estimation and identification methods are also available in the software. The emphasis on OKID was justified because it presents a better estimation of the impulse response functions when only a few noisy samples are available, which leads to better modal identification of a system [28].…”

Section: Development Of This Thesismentioning

confidence: 99%

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An Excursion in the Dynamics of Flexible Beams: From Modal Analysis to Nonlinear Modes

WAGNER¹

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Section: Development Of This Thesismentioning

confidence: 99%

“…(A-25) and take Eq. (A-9) into consideration, it follows that 28) shows that the eigenvectors qr are also orthogonal with respect to the stiffness matrix K s . Once again, the adopted normalization scheme was considered in Eq.…”

Section: A Linear Modesmentioning

confidence: 99%

An Excursion in the Dynamics of Flexible Beams: From Modal Analysis to Nonlinear Modes

WAGNER¹

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Model complexity optimization of equivalent dynamical linearization data models used in model‐free adaptive control

Salighe,

Söffker

2024

Proc Appl Math and Mech

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This paper discusses a strategy for optimizing the complexity of time‐varying data models as used in model‐free adaptive control (MFAC). Here the dynamic linearization in compact form (CFDL), partial form (PFDL), and full form (FFDL) are considered as data models used to describe input/output (I/O) data sets. These data models are built only for control purpose and can have various degrees of complexity depending on the size of the considered time‐window as well as the underlying algorithms. The methodology is to compare the performance of the data models according to an evaluation criterion, to analyze the order of different data models based on bias‐variance trade‐off, and to select the best‐performing model. The complexity of the selected model is compared with the reduced linear time‐invariant (LTI) model obtained by applying a combination of the eigensystem realization algorithm (ERA) and observer/Kalman filter identification (OKID) on the same I/O dataset. The I/O data are obtained from applying the MFAC controllers on a nonlinear three‐tank system (3TS) to track various desired references. The results indicate that the model complexity optimization can also be used for selecting an appropriate MFAC data model with optimal order to reduce the complexity and computational burden of the MFAC control algorithms.

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Benefits of OKID in system realization with low amount of samples

Cited by 2 publications

References 12 publications

An Excursion in the Dynamics of Flexible Beams: From Modal Analysis to Nonlinear Modes

An Excursion in the Dynamics of Flexible Beams: From Modal Analysis to Nonlinear Modes

Model complexity optimization of equivalent dynamical linearization data models used in model‐free adaptive control

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