Iterative learning control of nonlinear non-minimum phase systems and its application to system and model inversion
2001 (English)In: PROCEEDINGS OF THE 40TH IEEE CONFERENCE ON DECISION AND CONTROL, 2001, 4481-4482 p.Conference paper (Refereed)
In this contribution we present a model based method for reference tracking in the Iterative Learning Control (ILC) framework. The method can be applied to nonlinear, possibly non-minimum phase, systems. The idea is to use the inverse of a linearized model in the ILC update. In the non-minimum phase case, the batch property of ILC is explored by means of non-causal filtering. Apart from reference tracking, this method is useful for system and model inversion - a problem that arises in many disciplines where nonlinear systems and models are involved, e.g. maximum likelihood identification and input design for identification for control. The method is illustrated on a numerical example.
Place, publisher, year, edition, pages
2001. 4481-4482 p.
, IEEE CONFERENCE ON DECISION AND CONTROL - PROCEEDINGS, ISSN 0191-2216
Automation & Control Systems, Electrical & Electronic
Research subject SRA - ICT
IdentifiersURN: urn:nbn:se:kth:diva-26597DOI: 10.1109/.2001.980908ISI: 000178362200823ISBN: 0-7803-7061-9OAI: oai:DiVA.org:kth-26597DiVA: diva2:376392
40th IEEE Conference on Decision and Control ORLANDO, FL, DEC 04-07, 2001
FunderSwedish Research Council, 621-1999-179
QC 20101210 NR 201408042010-12-102010-11-252012-01-13Bibliographically approved