Monotonic convergence of iterative learning control systems with variable pass length
Seel T, Schauer T, Raisch J (2017)
Publication Type: Journal article
Publication year: 2017
Journal
Book Volume: 90
Pages Range: 409-422
Journal Issue: 3
DOI: 10.1080/00207179.2016.1183172
Abstract
A growing number of researchers consider iterative learning control (ILC) a promising tool for numerous control problems in biomedical application systems. We will briefly discuss why classical ILC theory is technically too restrictive for some of these applications. Subsequently, we will extend the classical ILC design in the lifted systems framework to the class of repetitive trajectory tracking tasks with variable pass length. We will analyse the closed-loop dynamics for two standard learning laws, and we will discuss in which sense the tracking error can be reduced by which controller design strategies. Necessary and sufficient conditions for monotonic convergence will be derived. We then summarise all results in a set of practical controller design guidelines. Finally, a simulation study is presented, which demonstrates the usefulness of these guidelines and illustrates the special dynamics that occur in variable pass length learning.
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Germany (DE)How to cite
APA:
Seel, T., Schauer, T., & Raisch, J. (2017). Monotonic convergence of iterative learning control systems with variable pass length. International Journal of Control, 90(3), 409-422. https://doi.org/10.1080/00207179.2016.1183172
MLA:
Seel, Thomas, Thomas Schauer, and Joerg Raisch. "Monotonic convergence of iterative learning control systems with variable pass length." International Journal of Control 90.3 (2017): 409-422.
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