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• W1933796868 abstract "This paper addresses the problem of model predictive control for a class of nonlinear systems which satisfies persistent excitation condition. The conditions under which a nonlinear system description can be handled are specified and two algorithms (one optimizing the first input sample and the other considering optimization of an M-sample subsequence of the input profile) solving the persistent excitation condition within a predictive controller for nonlinear systems are developed, both maximizing the smallest eigenvalue of the information matrix increase. The numerical experiments performed on a test-bed system demonstrate that the algorithms are able to successfully improve identifiability of a nonlinear system description while keeping the original controller performance degradation lower than arbitrarily chosen level." @default.
• W1933796868 created "2016-06-24" @default.
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• W1933796868 date "2015-09-01" @default.
• W1933796868 modified "2023-10-18" @default.
• W1933796868 title "MPC for a class of nonlinear systems with guaranteed identifiability" @default.
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• W1933796868 doi "https://doi.org/10.1109/cca.2015.7320627" @default.
• W1933796868 hasPublicationYear "2015" @default.
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