IEICE TRANSACTIONS on Fundamentals
Iterative Learning Control with Advanced Output Data for an Unknown Number of Non-minimum Phase Zeros
Summary :
This letter investigates an iterative learning control with advanced output data (ADILC) scheme for non-minimum phase (NMP) systems when the number of NMP zeros is unknown. ADILC has a simple learning structure that can be applied to both minimum phase and NMP systems. However, in the latter case, it is assumed that the number of NMP zeros is already known. In this paper, we propose an ADILC scheme in which the number of NMP zeros is unknown. Based on input-to-output mapping, the learning starts from the relative degree. When the input becomes larger than a certain upper bound, we redesign the input update law which consists of the relative degree and the estimated value for the number of NMP zeros.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E95-A No.8 pp.1416-1419
- Publication Date
- 2012/08/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E95.A.1416
- Type of Manuscript
- LETTER
- Category
- Systems and Control
Authors
Keyword
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Gu-Min JEONG, Chanwoo MOON, Hyun-Sik AHN, "Iterative Learning Control with Advanced Output Data for an Unknown Number of Non-minimum Phase Zeros" in IEICE TRANSACTIONS on Fundamentals,
vol. E95-A, no. 8, pp. 1416-1419, August 2012, doi: 10.1587/transfun.E95.A.1416.
Abstract: This letter investigates an iterative learning control with advanced output data (ADILC) scheme for non-minimum phase (NMP) systems when the number of NMP zeros is unknown. ADILC has a simple learning structure that can be applied to both minimum phase and NMP systems. However, in the latter case, it is assumed that the number of NMP zeros is already known. In this paper, we propose an ADILC scheme in which the number of NMP zeros is unknown. Based on input-to-output mapping, the learning starts from the relative degree. When the input becomes larger than a certain upper bound, we redesign the input update law which consists of the relative degree and the estimated value for the number of NMP zeros.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E95.A.1416/_p
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@ARTICLE{e95-a_8_1416,
author={Gu-Min JEONG, Chanwoo MOON, Hyun-Sik AHN, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Iterative Learning Control with Advanced Output Data for an Unknown Number of Non-minimum Phase Zeros},
year={2012},
volume={E95-A},
number={8},
pages={1416-1419},
abstract={This letter investigates an iterative learning control with advanced output data (ADILC) scheme for non-minimum phase (NMP) systems when the number of NMP zeros is unknown. ADILC has a simple learning structure that can be applied to both minimum phase and NMP systems. However, in the latter case, it is assumed that the number of NMP zeros is already known. In this paper, we propose an ADILC scheme in which the number of NMP zeros is unknown. Based on input-to-output mapping, the learning starts from the relative degree. When the input becomes larger than a certain upper bound, we redesign the input update law which consists of the relative degree and the estimated value for the number of NMP zeros.},
keywords={},
doi={10.1587/transfun.E95.A.1416},
ISSN={1745-1337},
month={August},}
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TY - JOUR
TI - Iterative Learning Control with Advanced Output Data for an Unknown Number of Non-minimum Phase Zeros
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1416
EP - 1419
AU - Gu-Min JEONG
AU - Chanwoo MOON
AU - Hyun-Sik AHN
PY - 2012
DO - 10.1587/transfun.E95.A.1416
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E95-A
IS - 8
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - August 2012
AB - This letter investigates an iterative learning control with advanced output data (ADILC) scheme for non-minimum phase (NMP) systems when the number of NMP zeros is unknown. ADILC has a simple learning structure that can be applied to both minimum phase and NMP systems. However, in the latter case, it is assumed that the number of NMP zeros is already known. In this paper, we propose an ADILC scheme in which the number of NMP zeros is unknown. Based on input-to-output mapping, the learning starts from the relative degree. When the input becomes larger than a certain upper bound, we redesign the input update law which consists of the relative degree and the estimated value for the number of NMP zeros.
ER -
English
