Self-optimizing robust nonlinear model predictive control

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6 Citations (Scopus)

Abstract

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
PublisherSpringer
Chapter2
Pages27-40
Number of pages14
ISBN (Electronic)978-3-642-01094-1
ISBN (Print)978-3-642-01093-4
DOIs
Publication statusPublished - 2009

Publication series

NameLecture Notes in Control and Information Sciences (LNCIS)
Volume384
ISSN (Print)0170-8643

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