Name
Abstract | ||
|---|---|---|
Iterative Learning Control (ILC) enables high tracking performance of batch repetitive processes. Common ILC approaches resort to discrete time system representations and hence are not able to guarantee good intersample behavior in case the underlying system evolves in continuous time. The aim of this paper is to explicitly deal with the intersample behavior in ILC. A multirate, parametric, and low-order approach to both identification for ILC and subsequent optimal ILC is presented that results in a low computational burden. The approach appropriately deals with the time-varying nature of multirate systems. The proposed multirate identification and ILC algorithms are shown to outperform common ILC approaches in a simulation example. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ACC.2009.5159951 | ACC'09 Proceedings of the 2009 conference on American Control Conference |
Keywords | DocType | ISSN |
good intersample behavior,discrete time system representation,multirate system,ilc algorithm,common ilc approach,optimal control,low-order system identification,common ilc,proposed multirate identification,intersample behavior,continuous time,low-order approach,parametric statistics,frequency,control systems,data mining,iterative learning control,predictive models,frequency control,time frequency analysis,computational modeling,identification,iterative methods,system identification,probability density function,sampling methods | Conference | 0743-1619 |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tom Oomen | 1 | 61 | 9.63 |
| Jeroen van de Wijdeven | 2 | 43 | 5.05 |
| Okko H. Bosgra | 3 | 109 | 16.28 |