System identification from multiple-trial data corrupted by non-repeating periodic disturbances

Minh Phan; Richard Longman; Soo Lee; Jae-Won Lee

International Journal of Applied Mathematics and Computer Science (2003)

  • Volume: 13, Issue: 2, page 185-192
  • ISSN: 1641-876X

Abstract

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Iterative learning and repetitive control aim to eliminate the effect of unwanted disturbances over repeated trials or cycles. The disturbance-free system model, if known, can be used in a model-based iterative learning or repetitive control system to eliminate the unwanted disturbances. In the case of periodic disturbances, although the unknown disturbance frequencies may be the same from trial to trial, the disturbance amplitudes, phases, and biases do not necessarily repeat. Furthermore, the system may not return to the same initial state at the end of each trial before starting the next trial. In spite of these constraints, this paper shows how to identify the system disturbance-free dynamics from disturbance-corrupted input-output data collected over multiple trials without having to measure the disturbances directly. The system disturbance-free model can then be used to identify the disturbances as well, for use in learning or repetitive control. This paper represents the first extension of the interaction matrix approach to the multiple-trial environment of iterative learning control.

How to cite

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Phan, Minh, et al. "System identification from multiple-trial data corrupted by non-repeating periodic disturbances." International Journal of Applied Mathematics and Computer Science 13.2 (2003): 185-192. <http://eudml.org/doc/207634>.

@article{Phan2003,
abstract = {Iterative learning and repetitive control aim to eliminate the effect of unwanted disturbances over repeated trials or cycles. The disturbance-free system model, if known, can be used in a model-based iterative learning or repetitive control system to eliminate the unwanted disturbances. In the case of periodic disturbances, although the unknown disturbance frequencies may be the same from trial to trial, the disturbance amplitudes, phases, and biases do not necessarily repeat. Furthermore, the system may not return to the same initial state at the end of each trial before starting the next trial. In spite of these constraints, this paper shows how to identify the system disturbance-free dynamics from disturbance-corrupted input-output data collected over multiple trials without having to measure the disturbances directly. The system disturbance-free model can then be used to identify the disturbances as well, for use in learning or repetitive control. This paper represents the first extension of the interaction matrix approach to the multiple-trial environment of iterative learning control.},
author = {Phan, Minh, Longman, Richard, Lee, Soo, Lee, Jae-Won},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {interaction matrix; iterative learning control; disturbance identification; system identification; repetitive control; linear system; multiple-trial approach; periodic disturbances},
language = {eng},
number = {2},
pages = {185-192},
title = {System identification from multiple-trial data corrupted by non-repeating periodic disturbances},
url = {http://eudml.org/doc/207634},
volume = {13},
year = {2003},
}

TY - JOUR
AU - Phan, Minh
AU - Longman, Richard
AU - Lee, Soo
AU - Lee, Jae-Won
TI - System identification from multiple-trial data corrupted by non-repeating periodic disturbances
JO - International Journal of Applied Mathematics and Computer Science
PY - 2003
VL - 13
IS - 2
SP - 185
EP - 192
AB - Iterative learning and repetitive control aim to eliminate the effect of unwanted disturbances over repeated trials or cycles. The disturbance-free system model, if known, can be used in a model-based iterative learning or repetitive control system to eliminate the unwanted disturbances. In the case of periodic disturbances, although the unknown disturbance frequencies may be the same from trial to trial, the disturbance amplitudes, phases, and biases do not necessarily repeat. Furthermore, the system may not return to the same initial state at the end of each trial before starting the next trial. In spite of these constraints, this paper shows how to identify the system disturbance-free dynamics from disturbance-corrupted input-output data collected over multiple trials without having to measure the disturbances directly. The system disturbance-free model can then be used to identify the disturbances as well, for use in learning or repetitive control. This paper represents the first extension of the interaction matrix approach to the multiple-trial environment of iterative learning control.
LA - eng
KW - interaction matrix; iterative learning control; disturbance identification; system identification; repetitive control; linear system; multiple-trial approach; periodic disturbances
UR - http://eudml.org/doc/207634
ER -

References

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