A tuning approach for offset-free MPC with conditional reference adaptation

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2015

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Model predictive control has become a widely accepted strategy in industrial applications in the recent years. Often mentioned reasons for the success are the optimization based on a system model, consideration of constraints and an intuitive tuning process. However, as soon as unknown disturbances or model plant mismatch have to be taken into account the tuning effort to achieve offset-free tracking increases. In this work a novel approach for offset-free MPC is presented, which divides the tuning in two steps, the setup of a nominal MPC loop and an external reference adaptation. The inner nominal loop addresses the performance targets in the nominal case, decouples the system and essentially leads to a first order response. The second outer loop enables offset-free tracking in case of unknown disturbances and consists of feedback controllers adapting the reference. Due to the mentioned properties these controllers can be tuned separate and by known guidelines. To address conditions with active input constraints, additionally a conditional reference adaptation scheme is introduced. The tuning strategy is evaluated on a simulated linear Wood-Berry binary distillation column example.
Original languageEnglish
Title of host publicationProceedings of the 19th IFAC World Congress
PublisherInternational Federation of Automatic Control
Publication date2014
Pages3062-3067
ISBN (print)978-3-902823-62-5
DOIs
StatePublished - 2014
Event19th World Congress of the International Federation of Automatic Control (IFAC 2014) - Cape Town, South Africa

Conference

Conference19th World Congress of the International Federation of Automatic Control (IFAC 2014)
CountrySouth Africa
CityCape Town
Period24/08/201429/08/2014
OtherThe theme of the congress: “Promoting automatic control for the benefit of humankind”
Internet address
CitationsWeb of Science® Times Cited: No match on DOI

    Keywords

  • Model predictive, Optimization-based control
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