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

Jakob Kjøbsted Huusom; Niels Kjølstad Poulsen; Sten Bay Jørgensen; John Bagterp Jørgensen

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

Jakob Kjøbsted Huusom
, Niels Kjølstad Poulsen
, Sten Bay Jørgensen
, John Bagterp Jørgensen

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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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 language	English
Title of host publication	Proceedings of the American Control Conference
Publisher	IEEE
Publication date	2010
Pages	2255-2360
ISBN (Print)	978-1-4244-7426-4
Publication status	Published - 2010
Event	American Control Conference (ACC 2010) - Baltimore, MD, United States Duration: 3 Jun 2010 → 2 Jul 2010 http://a2c2.org/conferences/acc2010/

Conference

Conference	American Control Conference (ACC 2010)
Country/Territory	United States
City	Baltimore, MD
Period	03/06/2010 → 02/07/2010
Internet address	http://a2c2.org/conferences/acc2010/

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Copyright 2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

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title = "Tuning of methods for offset free MPC based on ARX model representations",

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.",

author = "Huusom, \{Jakob Kj{\o}bsted\} and Poulsen, \{Niels Kj{\o}lstad\} and J{\o}rgensen, \{Sten Bay\} and J{\o}rgensen, \{John Bagterp\}",

note = "Copyright 2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.; American Control Conference (ACC 2010), ACC2010 ; Conference date: 03-06-2010 Through 02-07-2010",

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language = "English",

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AU - Huusom, Jakob Kjøbsted

AU - Poulsen, Niels Kjølstad

AU - Jørgensen, Sten Bay

AU - Jørgensen, John Bagterp

N1 - Copyright 2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2010

Y1 - 2010

N2 - 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.

AB - 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.

M3 - Article in proceedings

SN - 978-1-4244-7426-4

SP - 2255

EP - 2360

BT - Proceedings of the American Control Conference

PB - IEEE

T2 - American Control Conference (ACC 2010)

Y2 - 3 June 2010 through 2 July 2010

ER -

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

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