On the interaction between theory experiments and simulation in developing practical learning control algorithms

Richard Longman

International Journal of Applied Mathematics and Computer Science (2003)

  • Volume: 13, Issue: 1, page 101-111
  • ISSN: 1641-876X

Abstract

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Iterative learning control (ILC) develops controllers that iteratively adjust the command to a feedback control system in order to converge to zero tracking error following a specific desired trajectory. Unlike optimal control and other control methods, the iterations are made using the real world in place of a computer model. If desired, the learning process can be conducted both in the time domain during each iteration and in repetitions, making ILC a 2D system. Because ILC iterates with the real world, and aims for zero error, the field pushes the limits of theory, modeling, and simulation, to predict the behavior when applied in the real world. It is the thesis of this paper that in order to make significant progress in this field it is essential that the research effort employ a coordinated simultaneous synergistic effort involving theory, experiments, and serious simulations. Otherwise, one very easily expends effort on something that seems fundamental from the theoretical perspective, but in fact has very little relevance to the performance in real world applications.

How to cite

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Longman, Richard. "On the interaction between theory experiments and simulation in developing practical learning control algorithms." International Journal of Applied Mathematics and Computer Science 13.1 (2003): 101-111. <http://eudml.org/doc/207618>.

@article{Longman2003,
abstract = {Iterative learning control (ILC) develops controllers that iteratively adjust the command to a feedback control system in order to converge to zero tracking error following a specific desired trajectory. Unlike optimal control and other control methods, the iterations are made using the real world in place of a computer model. If desired, the learning process can be conducted both in the time domain during each iteration and in repetitions, making ILC a 2D system. Because ILC iterates with the real world, and aims for zero error, the field pushes the limits of theory, modeling, and simulation, to predict the behavior when applied in the real world. It is the thesis of this paper that in order to make significant progress in this field it is essential that the research effort employ a coordinated simultaneous synergistic effort involving theory, experiments, and serious simulations. Otherwise, one very easily expends effort on something that seems fundamental from the theoretical perspective, but in fact has very little relevance to the performance in real world applications.},
author = {Longman, Richard},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {ILC; learning transients; iterative learning control; 2D systems; stability; zero tracking error},
language = {eng},
number = {1},
pages = {101-111},
title = {On the interaction between theory experiments and simulation in developing practical learning control algorithms},
url = {http://eudml.org/doc/207618},
volume = {13},
year = {2003},
}

TY - JOUR
AU - Longman, Richard
TI - On the interaction between theory experiments and simulation in developing practical learning control algorithms
JO - International Journal of Applied Mathematics and Computer Science
PY - 2003
VL - 13
IS - 1
SP - 101
EP - 111
AB - Iterative learning control (ILC) develops controllers that iteratively adjust the command to a feedback control system in order to converge to zero tracking error following a specific desired trajectory. Unlike optimal control and other control methods, the iterations are made using the real world in place of a computer model. If desired, the learning process can be conducted both in the time domain during each iteration and in repetitions, making ILC a 2D system. Because ILC iterates with the real world, and aims for zero error, the field pushes the limits of theory, modeling, and simulation, to predict the behavior when applied in the real world. It is the thesis of this paper that in order to make significant progress in this field it is essential that the research effort employ a coordinated simultaneous synergistic effort involving theory, experiments, and serious simulations. Otherwise, one very easily expends effort on something that seems fundamental from the theoretical perspective, but in fact has very little relevance to the performance in real world applications.
LA - eng
KW - ILC; learning transients; iterative learning control; 2D systems; stability; zero tracking error
UR - http://eudml.org/doc/207618
ER -

References

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