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• W2038187336 abstract "This paper presents the design of iterative learning control based on Quadratic performance criterion (Q-ILC) for linear systems subject to additive uncertainty. Robust Q-ILC design can be cast as a min-max problem. We propose a novel approach which employs an upper bound of the worst-case error, then formulates a nonconvex quadratic minimization problem to get the update of iterative control inputs. Applying Langrange duality, the Lagrange dual function of the nonconvex quadratic problem is equivalent to a convex optimization over linear matrix inequalities (LMIs). An LMI algorithm with convergence properties is then given for the robust Q-ILC. Finally, we provide a numerical example to illustrate the effectiveness of the proposed method." @default.
• W2038187336 created "2016-06-24" @default.
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• W2038187336 date "2008-12-01" @default.
• W2038187336 modified "2023-09-27" @default.
• W2038187336 title "An LMI approach for robust Iterative Learning Control with Quadratic performance criterion" @default.
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• W2038187336 doi "https://doi.org/10.1109/icarcv.2008.4795802" @default.
• W2038187336 hasPublicationYear "2008" @default.
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