Norm optimal ILC with time-varying weighting matrices

Kira Barton, Andrew Alleyne

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

7 Scopus citations


In this paper, we focus on improving performance and robustness in precision motion control (PMC) of multi-axis systems through the use of time-varying weighting matrices. A Norm Optimal (N.O.) framework is used to design optimal learning filters based on design objectives. The general N.O. framework is reformatted to include time-varying weighting matrices which enable the controller to take the trajectory, position-dependent dynamics, and time-varying disturbances into consideration when designing the optimal learning controller. A general approach for designing the different weighting matrices is included. The time-varying weighting approach of this framework enables one to focus on individual components that affect the system at different times throughout the trajectory independently. The performance benefits of timevarying weighting matrices are illustrated through simulation and experimental testing on a multi-axis robotic testbed.

Original languageEnglish (US)
Title of host publication2009 American Control Conference, ACC 2009
Number of pages7
StatePublished - 2009
Externally publishedYes
Event2009 American Control Conference, ACC 2009 - St. Louis, MO, United States
Duration: Jun 10 2009Jun 12 2009

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619


Other2009 American Control Conference, ACC 2009
Country/TerritoryUnited States
CitySt. Louis, MO


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