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Licensed Unlicensed Requires Authentication Published by De Gruyter (O) September 9, 2021

Probabilistic model predictive control for extended prediction horizons

Probabilistische Modellprädiktive Regelung für lange Prädiktionshorizonte
Tim Brüdigam, Johannes Teutsch, Dirk Wollherr, Marion Leibold and Martin Buss


Detailed prediction models with robust constraints and small sampling times in Model Predictive Control yield conservative behavior and large computational effort, especially for longer prediction horizons. Here, we extend and combine previous Model Predictive Control methods that account for prediction uncertainty and reduce computational complexity. The proposed method uses robust constraints on a detailed model for short-term predictions, while probabilistic constraints are employed on a simplified model with increased sampling time for long-term predictions. The underlying methods are introduced before presenting the proposed Model Predictive Control approach. The advantages of the proposed method are shown in a mobile robot simulation example.


Detaillierte Prädiktionsmodelle mit robusten Nebenbedingungen in der Modellprädiktiven Regelung führen zu konservativem Verhalten und hohem Rechenaufwand, besonders für lange Prädiktionshorizonte. In dieser Arbeit werden die Vorteile bisheriger Arbeiten zur Modellprädiktiven Regelung erweitert und kombiniert, um Prädiktionsunsicherheit zu berücksichtigen und den Rechenaufwand zu reduzieren. Die vorgeschlagene Methode nutzt robuste Nebenbedingungen und ein detailliertes Modell für kurzfristige Prädiktionen, während probabilistische Nebenbedingungen, ein vereinfachtes Modell und eine größere Abtastzeit für die langfristige Prädiktion genutzt werden. Die Vorteile der neuen Methode werden in einem Simulationsbeispiel analysiert.


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Received: 2021-01-30
Accepted: 2021-08-16
Published Online: 2021-09-09
Published in Print: 2021-09-27

© 2021 Walter de Gruyter GmbH, Berlin/Boston

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