Generalised parameterisation for MPC

  • Bilal Khan*
  • , Anthony Rossiter
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

The paper generalises approaches to predictive control based on Laguerre and Kautz functions. It is shown that La- guerre and Kautz are special cases of generalised orthonor- mal basis functions and thus one can give a more gen- eral parameterisation using higher order orthonormal ba- sis functions. Specifically, a simple but efficient algorithm that uses generalised functions to parameterise the degrees of freedom in an optimal predictive control is presented. The efficacy of the proposed parameterisation within exist- ing predictive control algorithms that use a similar strategy, is demonstrated by examples.

Original languageEnglish
Title of host publicationProceedings of the 13th IASTED International Conference on Intelligent Systems and Control, ISC 2011
Pages120-126
Number of pages7
DOIs
StatePublished - 2011
Externally publishedYes
Event13th IASTED International Conference on Intelligent Systems and Control, ISC 2011 - Cambridge, United Kingdom
Duration: 11 Jul 201113 Jul 2011

Publication series

NameProceedings of the IASTED International Conference on Intelligent Systems and Control
ISSN (Print)1025-8973

Conference

Conference13th IASTED International Conference on Intelligent Systems and Control, ISC 2011
Country/TerritoryUnited Kingdom
CityCambridge
Period11/07/1113/07/11

Keywords

  • Feasibility
  • Generalised parameterisation
  • MPC

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Software
  • Control and Systems Engineering
  • Modeling and Simulation
  • Artificial Intelligence

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