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 vol.5 p.Conference paper (Refereed)
In this contribution we place ILC in the realm of numerical optimization. This clarifies the role played by the design variables and how they affect e.g. convergence properties. We give a model based interpretation of these design variables and also a sufficient condition for convergence of ILC which is similar in spirit to the sufficient and necessary condition previously derived for linear systems. This condition shows that the desired performance has to be traded against modelling accuracy. Finally, one of the main benefits of ILC when non-minimum phase systems are concerned, the possibility of non-causal control, is given a comprehensive coverage.
Place, publisher, year, edition, pages
2001. 4481-4482 vol.5 p.
Iterative learning control, Non-linear systems, Model inversion
National CategoryEngineering and Technology Control Engineering
IdentifiersURN: urn:nbn:se:liu:diva-29600DOI: 10.1109/.2001.980908Local ID: 14978ISBN: 0-7803-7061-9OAI: oai:DiVA.org:liu-29600DiVA: diva2:250417
40th IEEE Conference on Decision and Control, Orlando, FL, USA, December, 2001