An improved method for calculating iterative learning control convergence rate

Kira Barton, Andrew Alleyne, Doug Bristow

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In Iterative Learning Control (ILC). the lifted system is often used in design and analysis to determine convergence rate of the learning algorithm. Computation of the convergence rate in the lifted setting requires construction of large NxN matrices, where N is the number of data points in an iteration. The convergece rate computation is O(N2) and is typically limited to short iteration lengths because of computational memory constraints. In this article, we present an alternative method for calculating the convergence rate without the need of large matrix calculations. This method uses the implicitly restarted Arnoldi method and dynamic simulations to calculate the ILC norm, reducing the calculatin to O(N). In addition to faster computation, we are able to calculate the convergence rate for long iteration lengths. This method is presented for multi-input multi-output, linear time-varying discrete-time systems.

Original languageEnglish (US)
Title of host publication2008 Proceedings of the ASME Dynamic Systems and Control Conference, DSCC 2008
Pages97-104
Number of pages8
EditionPART A
StatePublished - 2009
Event2008 ASME Dynamic Systems and Control Conference, DSCC 2008 - Ann Arbor, MI, United States
Duration: Oct 20 2008Oct 22 2008

Publication series

Name2008 Proceedings of the ASME Dynamic Systems and Control Conference, DSCC 2008
NumberPART A

Other

Other2008 ASME Dynamic Systems and Control Conference, DSCC 2008
Country/TerritoryUnited States
CityAnn Arbor, MI
Period10/20/0810/22/08

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering
  • Mechanical Engineering

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