Normalized Iterative Feedback Tuning with Time Constraints

SungEun JO; Sang Woo KIM; Jin Soo LEE

Normalized Iterative Feedback Tuning with Time Constraints

SungEun JO, Sang Woo KIM, Jin Soo LEE

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This paper provides a normalized Iterative Feedback Tuning (IFT) method that assures the boundedness of the gradient vector estimate (ρ) and the Hessian matrix estimate without the assumption that the internal signals are bounded. The proposed method uses the unbiased Gauss-Newton direction by the addition of the 4-th experiment. We also present blended control criteria and a PID-like controller as new design choices. In examples, the normalized IFT method results in a good convergence although the internal signal or the measurement noise variance is large.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E84-A No.2 pp.681-687

Publication Date: 2001/02/01

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Type of Manuscript: LETTER

Category: Systems and Control

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SungEun JO, Sang Woo KIM, Jin Soo LEE, "Normalized Iterative Feedback Tuning with Time Constraints" in IEICE TRANSACTIONS on Fundamentals, vol. E84-A, no. 2, pp. 681-687, February 2001, doi: .
Abstract: This paper provides a normalized Iterative Feedback Tuning (IFT) method that assures the boundedness of the gradient vector estimate (ρ) and the Hessian matrix estimate without the assumption that the internal signals are bounded. The proposed method uses the unbiased Gauss-Newton direction by the addition of the 4-th experiment. We also present blended control criteria and a PID-like controller as new design choices. In examples, the normalized IFT method results in a good convergence although the internal signal or the measurement noise variance is large.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e84-a_2_681/_p

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@ARTICLE{e84-a_2_681,
author={SungEun JO, Sang Woo KIM, Jin Soo LEE, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Normalized Iterative Feedback Tuning with Time Constraints},
year={2001},
volume={E84-A},
number={2},
pages={681-687},
abstract={This paper provides a normalized Iterative Feedback Tuning (IFT) method that assures the boundedness of the gradient vector estimate (ρ) and the Hessian matrix estimate without the assumption that the internal signals are bounded. The proposed method uses the unbiased Gauss-Newton direction by the addition of the 4-th experiment. We also present blended control criteria and a PID-like controller as new design choices. In examples, the normalized IFT method results in a good convergence although the internal signal or the measurement noise variance is large.},
keywords={},
doi={},
ISSN={},
month={February},}

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TY - JOUR
TI - Normalized Iterative Feedback Tuning with Time Constraints
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 681
EP - 687
AU - SungEun JO
AU - Sang Woo KIM
AU - Jin Soo LEE
PY - 2001
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E84-A
IS - 2
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - February 2001
AB - This paper provides a normalized Iterative Feedback Tuning (IFT) method that assures the boundedness of the gradient vector estimate (ρ) and the Hessian matrix estimate without the assumption that the internal signals are bounded. The proposed method uses the unbiased Gauss-Newton direction by the addition of the 4-th experiment. We also present blended control criteria and a PID-like controller as new design choices. In examples, the normalized IFT method results in a good convergence although the internal signal or the measurement noise variance is large.
ER -