Iterative Learning Control - monotonicity and optimization

David H. Owens; Steve Daley

International Journal of Applied Mathematics and Computer Science (2008)

  • Volume: 18, Issue: 3, page 279-293
  • ISSN: 1641-876X

Abstract

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The area if Iterative Learning Control (ILC) has great potential for applications to systems with a naturally repetitive action where the transfer of data from repetition (trial or iteration) can lead to substantial improvements in tracking performance. There are several serious issues arising from the "2D" structure of ILC and a number of new problems requiring new ways of thinking and design. This paper introduces some of these issues from the point of view of the research group at Sheffield University and concentrates on linear systems and the potential for the use of optimization methods and switching strategies to achieve effective control.

How to cite

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David H. Owens, and Steve Daley. "Iterative Learning Control - monotonicity and optimization." International Journal of Applied Mathematics and Computer Science 18.3 (2008): 279-293. <http://eudml.org/doc/207885>.

@article{DavidH2008,
abstract = {The area if Iterative Learning Control (ILC) has great potential for applications to systems with a naturally repetitive action where the transfer of data from repetition (trial or iteration) can lead to substantial improvements in tracking performance. There are several serious issues arising from the "2D" structure of ILC and a number of new problems requiring new ways of thinking and design. This paper introduces some of these issues from the point of view of the research group at Sheffield University and concentrates on linear systems and the potential for the use of optimization methods and switching strategies to achieve effective control.},
author = {David H. Owens, Steve Daley},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {iterative learning control; optimization; monotonicity; robust control},
language = {eng},
number = {3},
pages = {279-293},
title = {Iterative Learning Control - monotonicity and optimization},
url = {http://eudml.org/doc/207885},
volume = {18},
year = {2008},
}

TY - JOUR
AU - David H. Owens
AU - Steve Daley
TI - Iterative Learning Control - monotonicity and optimization
JO - International Journal of Applied Mathematics and Computer Science
PY - 2008
VL - 18
IS - 3
SP - 279
EP - 293
AB - The area if Iterative Learning Control (ILC) has great potential for applications to systems with a naturally repetitive action where the transfer of data from repetition (trial or iteration) can lead to substantial improvements in tracking performance. There are several serious issues arising from the "2D" structure of ILC and a number of new problems requiring new ways of thinking and design. This paper introduces some of these issues from the point of view of the research group at Sheffield University and concentrates on linear systems and the potential for the use of optimization methods and switching strategies to achieve effective control.
LA - eng
KW - iterative learning control; optimization; monotonicity; robust control
UR - http://eudml.org/doc/207885
ER -

References

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  33. Ratcliffe J., Harte T.J., Hätönen J., Lewin P.Rogers E. and Owens D.H. (2004). Practical implementation of a model inverse optimal iterative learning controller on a gantry robot, Proceedings of the IFAC Workshop on Periodic Systems (PSYCO 04), Yokohama, Japan. 
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  39. Zilouchian A. (1994). An iterative learning control technique for a dual arm robotic system, Proceedings of the 1994 IEEE International Conference on Robotics and Automation, San Diego, CA, USA, pp. 1528-1533. 

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