Input-to-state stabilizing sub-optimal nonlinear MPC algorithms with an application to DC-DC converters

M. Lazar, B.J.P. Roset, W.P.M.H. Heemels, H. Nijmeijer, P.P.J. Bosch, van den

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Abstract

Abstract: This paper focuses on the synthesis of computationally friendly sub-optimal nonlinear Model Predictive Control (MPC) algorithms with guaranteed robust stability. The input-to-state stability framework is employed to analyze the robustness of the resulting MPC closed-loop systems. Two new sub-optimal nonlinear MPC schemes are proposed, based on a contraction argument and an artificial Lyapunov function, respectively. The developed theory is illustrated by applying it to control a Buck-Boost DC-DC converter. Copyright °c 2006 IFAC
Original languageEnglish
Title of host publicationProceedings of the 1st IFAC Workshop on Nonlinear Model Predictive Control for Fast Systems, 9-11 October 2006, Grenoble, France
Place of PublicationGrenoble, France
PublisherIFAC
Pages83-88
Publication statusPublished - 2006
Event1st IFAC Workshop on Nonlinear Model Predictive Control for Fast Systems - Grenoble, France
Duration: 9 Oct 200611 Oct 2006

Workshop

Workshop1st IFAC Workshop on Nonlinear Model Predictive Control for Fast Systems
CountryFrance
CityGrenoble
Period9/10/0611/10/06

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    Lazar, M., Roset, B. J. P., Heemels, W. P. M. H., Nijmeijer, H., & Bosch, van den, P. P. J. (2006). Input-to-state stabilizing sub-optimal nonlinear MPC algorithms with an application to DC-DC converters. In Proceedings of the 1st IFAC Workshop on Nonlinear Model Predictive Control for Fast Systems, 9-11 October 2006, Grenoble, France (pp. 83-88). Grenoble, France: IFAC.