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CDC08 Paper Abstract


Paper WeA15.4

Bristow, Douglas A. (Missouri University of Science and Technology), Hencey, Brandon (University of Illinois)

A Q, L Factorization of Norm-Optimal Iterative Learning Control

Scheduled for presentation during the Regular Session "Iterative Learning Control" (WeA15), Wednesday, December 10, 2008, 10:30−10:50, Coral Gallery IIIA

47th IEEE Conference on Decision and Control, December 9-11, 2008, Fiesta Americana Grand Coral Beach, Cancun, Mexico

This information is tentative and subject to change. Compiled on May 15, 2021

Keywords Iterative learning control, Optimal control


In this paper we consider the Norm-Optimal Iterative Learning Control (ILC) problem for discrete-time linear multi-input, multi-output systems. The solution to this problem is well known and naturally factors into a form with a filter on the previous control, Lu and a filter on the previous error, Le. We show that this solution can always be factored into a Q,L form where Q filters the previous control and QL filters the previous error. This latter form is popularized with frequency domain ILC designs, and this common factorization suggests some general relationships between Norm-Optimal and frequency domain design, which are explored. Although the Q,L factorization is well known for some special cases, the results here are general and include differently dimensioned control and observation windows.



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