Self-optimizing robust nonlinear model predictive control

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6 Citations (Scopus)


This paper presents a novel method for designing robust MPC schemes that are self-optimizing in terms of disturbance attenuation. The method employs convex control Lyapunov functions and disturbance bounds to optimize robustness of the closed-loop system on-line, at each sampling instant - a unique feature in MPC. Moreover, the proposed MPC algorithm is computationally efficient for nonlinear systems that are affine in the control input and it allows for a decentralized implementation.
Original languageEnglish
Title of host publicationNonlinear model predictive control
Subtitle of host publicationtowards new challenging applications
EditorsLalo Magni, Martino Raimondo, Frank Allgöwer
Place of PublicationBerlin
Number of pages14
ISBN (Electronic)978-3-642-01094-1
ISBN (Print)978-3-642-01093-4
Publication statusPublished - 2009

Publication series

NameLecture Notes in Control and Information Sciences (LNCIS)
ISSN (Print)0170-8643


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