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Nussbaum gain based iterative learning control for a class of multi-input multi-output nonlinear systems.
Jiang, Ping Chen, H.
Jiang, Ping
Chen, H.
Publication Date
2005
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© 2005 IEEE. Reprinted from 44th IEEE Conference on Decision and Control, and
the European Control Conference 2005. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Bradford's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
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Abstract
An adaptive iterative learning control(ILC)
approach is proposed for a class of multi-input multi-output
(MIMO) uncertain nonlinear systems without prior knowledge
about system control gain matrices. The Nussbaum-type gain
and the positive definite discrete matrix kernel are proposed for
dealing with selection of the unknown control gain and learning
of the repeatable uncertainties, respectively. Asymptotic
convergence for a trajectory tracking within a finite time
interval is achieved through repetitive tracking. Simulations are
carried out to show the validity of the proposed control method.
Version
published version paper
Citation
Jiang, P. and Chen, H. (2005). Nussbaum gain based iterative learning control for a class of multi-input multi-output nonlinear systems. In: Proceedings of the
44th IEEE Conference on Decision and Control, and the European Control Conference 2005, Seville, Spain, December 12-15, 2005. pp. 2439- 2444.
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Conference paper