Paper Info
Title
System Identification and Low-Order Optimal Control of Intersample Behavior in ILC
Abstract
Although iterative learning control (ILC) algorithms enable performance improvement for batch repetitive systems using limited system knowledge, at least an approximate model is essential. The aim of the present technical note is to develop an ILC framework for sampled-data systems, i.e., by incorporating the intersample response. Hereto, a novel parametric system identification procedure and a low-order optimal ILC controller synthesis procedure are presented that both incorporate the intersample behavior in a multirate framework. The results include i) improved computational properties compared to prior optimization-based ILC algorithms, and ii) improved performance of sampled-data systems compared to common discrete time ILC. These results are confirmed in a simulation example.
Year
DOI
Venue
2011
10.1109/TAC.2011.2160596
Automatic Control, IEEE Transactions
Keywords
Field
DocType
control system synthesis,learning systems,optimal control,parameter estimation,sampled data systems,self-adjusting systems,batch repetitive system,intersample behavior,intersample response,iterative learning control algorithm,low-order optimal ILC controller synthesis,multirate framework,parametric system identification procedure,sampled data system,Iterative learning control (ILC)
Control theory,Optimal control,Control theory,Computer science,Control engineering,Parametric statistics,Iterative learning control,Estimation theory,Discrete time and continuous time,System identification,Performance improvement
Journal
Volume
Issue
ISSN
56
11
0018-9286
Citations 
PageRank 
References 
8
0.69
11
Authors
3
Name
Order
Citations
PageRank
Oomen, T.19517.42
Jeroen van de Wijdeven2435.05
Bosgra, O.H.38120.46