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

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Abstract

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
PublisherIEEE
Publication date2010
Pages2255-2360
ISBN (Print)978-1-4244-7426-4
Publication statusPublished - 2010
EventAmerican Control Conference (ACC 2010) - Baltimore, MD, United States
Duration: 3 Jun 20102 Jul 2010
http://a2c2.org/conferences/acc2010/

Conference

ConferenceAmerican Control Conference (ACC 2010)
CountryUnited States
CityBaltimore, MD
Period03/06/201002/07/2010
Internet address

Bibliographical note

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