Tuning of methods for offset free MPC based on ARX model representations

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2010


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In this paper we investigate model predictive control (MPC) based on ARX models. ARX models can be identified from data using convex optimization technologies and is linear in the system parameters. Compared to other model parameterizations this feature is an advantage in embedded applications for robust and automatic system identification. Standard MPC is not able to reject a sustained, unmeasured, non zero mean disturbance and will therefore not provide offset free tracking. Offset free tracking can be guaranteed for this type of disturbances if Δ variables are used or if the state space is extended with a disturbance model state. The relation between the base case and the two extended methods are illustrated which provides good understanding and a platform for discussing tuning for good closed loop performance.
Original languageEnglish
Title of host publicationProceedings of the American Control Conference
Publication date2010
ISBN (print)978-1-4244-7426-4
StatePublished - 2010
EventAmerican Control Conference (ACC 2010) - Baltimore, MD, United States


ConferenceAmerican Control Conference (ACC 2010)
CountryUnited States
CityBaltimore, MD
Internet address

Bibliographical note

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