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dc.contributor.authorDriessen, Brian J.
dc.contributor.authorSadegh, Nader
dc.contributor.authorKwok, Kwan
dc.date.accessioned2017-04-17T14:40:08Z
dc.date.available2017-04-17T14:40:08Z
dc.date.issued2017-03
dc.identifier.citationDriessen, B., Sadegh, N., and Kwok, K. (2017) Bounded-input iterative learning control: Robust stabilization via a minimax approach. Int. J. Adapt. Control Signal Process., 31: 417–428en_US
dc.identifier.issn0890-6327
dc.identifier.otherWOS:000395473600007
dc.identifier.urihttp://onlinelibrary.wiley.com/doi/10.1002/acs.2710/full
dc.identifier.urihttp://hdl.handle.net/10057/12944
dc.descriptionClick on the URL link to access the article (may not be free).en_US
dc.description.abstractIn this paper, we consider the design problem of making the convergence of the bounded-input, multi-input iterative learning controller presented in our previous work robust to errors in the model-based value of the input-output Jacobian matrix via a minimax (min-max or 'minimize the worst case') approach. We propose to minimize the worst case (largest) value of the infinity-norm of the matrix whose norm being less then unity implies convergence of the controller. This matrix is the one associated with monotonicity of a sequence of input error norms. The input-output Jacobian uncertainty is taken to be an additive linear one. Theorem 3.1 and its proof show that the worst-case infinity-norm is actually minimized by choosing either the inverse of the centroid of the set of possible input-output Jacobians or a zero matrix. And an explicit expression is given for both the criteria used to choose between the two matrices and the resulting minimum worst-case infinity norm. We showed previously that the matrix norm condition associated with monotonicity of a sequence of output-error norms is not sufficient to assure convergence of the bounded-input controller. The importance of knowing which norm condition is the relevant one is demonstrated by showing that the set of minimizers of the minimax problem formulated with the wrong norm does not contain in general minimizers of the maximum relevant norm and moreover can lead to a gain matrix that destroys the assured convergence of the bounded-input controller given in previous work.en_US
dc.description.sponsorshipUnited States Department of Energy under Contract DE-AC04-94AL85000.en_US
dc.language.isoen_USen_US
dc.publisherJohn Wiley & Sons, Ltd.en_US
dc.relation.ispartofseriesInternational Journal ofAdaptive Control and Signal Processing;v.31:no.3
dc.subjectIterative learning controlen_US
dc.subjectRobust stabilityen_US
dc.subjectBounded inputsen_US
dc.subjectMinimaxen_US
dc.titleBounded-input iterative learning control: robust stabilization via a minimax approachen_US
dc.typeArticleen_US
dc.rights.holder© 2016 John Wiley & Sons, Ltd.en_US


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