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On the coupling of model predictive control and robust Kalman filtering
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
Univ Padua, Italy.
2018 (English)In: IET Control Theory & Applications, ISSN 1751-8644, E-ISSN 1751-8652, Vol. 12, no 13, p. 1873-1881Article in journal (Refereed) Published
Abstract [en]

Model predictive control (MPC) represents nowadays one of the main methods employed for process control in industry. Its strong suits comprise a simple algorithm based on a straightforward formulation and the flexibility to deal with constraints. On the other hand, it can be questioned its robustness regarding model uncertainties and external noises. Thus, a lot of efforts have been spent in the past years into the search of methods to address these shortcomings. In this study, the authors propose a robust MPC controller which stems from the idea of adding robustness in the prediction phase of the algorithm while leaving the core of MPC untouched. More precisely, they consider a robust Kalman filter that has been recently introduced and they further extend its usability to feedback control systems. Overall the proposed control algorithm allows to maintain all of the advantages of MPC with an additional improvement in performance and without any drawbacks in terms of computational complexity. To test the actual reliability of the algorithm, they apply it to control a servomechanism system characterised by non-linear dynamics.

Place, publisher, year, edition, pages
INST ENGINEERING TECHNOLOGY-IET , 2018. Vol. 12, no 13, p. 1873-1881
Keywords [en]
predictive control; servomechanisms; feedback; Kalman filters; robust control; filtering theory; optimal control; nonlinear dynamical systems; uncertain systems; prediction phase; control systems; control algorithm; model predictive control; robust Kalman filtering; process control; model uncertainties; robust MPC controller; MPC; external noises; feedback control systems; servomechanism system; nonlinear dynamics
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-150845DOI: 10.1049/iet-cta.2017.1074ISI: 000441863900010OAI: oai:DiVA.org:liu-150845DiVA, id: diva2:1245924
Available from: 2018-09-06 Created: 2018-09-06 Last updated: 2018-09-06

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CiteExportLink to record
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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf