A model-free approach for auto-tuning of model predictive control

Nhat Tran, J. Scholten, Leyla Ozkan, A.C.P.M. Backx

Research output: Contribution to journalConference articleAcademicpeer-review

6 Citations (Scopus)

Abstract

A two-layer approach for the auto-tuning of model predictive control (MPC) is proposed. The bottom layer computes the weighting matrices of the cost function from a desired closed-loop bandwidth while the top layer aims at finding the optimal bandwidth. This optimum corresponds to the optimal balance between the robustness and nominal performance of the closed-loop system. To find the optimal bandwidth, the extremum seeking (ES) algorithm, a form of non-model-based adaptive optimisation, is proposed. The auto-tuning approach is tested on a binary distillation column model. It is shown that the auto-tuning approach enables the MPC system to track its optimal closed-loop bandwidth and therefore obtain the minimum output variance.

Original languageEnglish
Pages (from-to)2189-2194
Number of pages6
JournalIFAC-PapersOnLine
Volume47
Issue number3
DOIs
Publication statusPublished - 2014
Event19th IFAC World Congress on International Federation of Automatic Control ( IFAC 2014) - Cape Town International Convention Centre, Cape Town, South Africa
Duration: 24 Aug 201429 Aug 2014
Conference number: 19
http://www.ifac2014.org

Keywords

  • Auto-tuning
  • Closed-loop bandwidth
  • Extremum seeking
  • Model predictive control

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