Minimum variance performance map for constrained model predictive control

Christopher A. HARRISON; S. Joe QIN

doi:10.1016/j.jprocont.2009.04.005

Minimum variance performance map for constrained model predictive control

Christopher A. HARRISON, S. Joe QIN^*

^*Corresponding author for this work

Research output: Journal Publications › Journal Article (refereed) › peer-review

44 Citations (Scopus)

Abstract

A minimum variance performance map is introduced for constrained linear model predictive control (MPC). The minimum variance performance map provides a demonstration of the effect of constraints in an MPC on the best achievable controller performance. The constrained minimum variance controller is formulated for the MPC system to be monitored. Using multi-parametric quadratic programming (mp-QP), the linear, piecewise control law is obtained for the constrained minimum variance controller. The linear, piecewise control law is used with a Kalman filter to obtain the minimum output variance in each region of the state space partition. The minimum variance performance map is demonstrated on a second order process with a constraint on the input amplitude. © 2009 Elsevier Ltd. All rights reserved.

Original language	English
Pages (from-to)	1199-1204
Number of pages	6
Journal	Journal of Process Control
Volume	19
Issue number	7
Early online date	21 May 2009
DOIs	https://doi.org/10.1016/j.jprocont.2009.04.005
Publication status	Published - Jul 2009
Externally published	Yes

Bibliographical note

This research was supported by a National Science Foundation Graduate Fellowship, a National Science Defense and Engineering Graduate Fellowship, a National Science Foundation grant under DMI-0432433, and the members of the Texas-Wisconsin-California Control Consortium.

Keywords

MPC performance map
Minimum variance
Performance monitoring

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10.1016/j.jprocont.2009.04.005

Cite this

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abstract = "A minimum variance performance map is introduced for constrained linear model predictive control (MPC). The minimum variance performance map provides a demonstration of the effect of constraints in an MPC on the best achievable controller performance. The constrained minimum variance controller is formulated for the MPC system to be monitored. Using multi-parametric quadratic programming (mp-QP), the linear, piecewise control law is obtained for the constrained minimum variance controller. The linear, piecewise control law is used with a Kalman filter to obtain the minimum output variance in each region of the state space partition. The minimum variance performance map is demonstrated on a second order process with a constraint on the input amplitude. {\textcopyright} 2009 Elsevier Ltd. All rights reserved.",

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N2 - A minimum variance performance map is introduced for constrained linear model predictive control (MPC). The minimum variance performance map provides a demonstration of the effect of constraints in an MPC on the best achievable controller performance. The constrained minimum variance controller is formulated for the MPC system to be monitored. Using multi-parametric quadratic programming (mp-QP), the linear, piecewise control law is obtained for the constrained minimum variance controller. The linear, piecewise control law is used with a Kalman filter to obtain the minimum output variance in each region of the state space partition. The minimum variance performance map is demonstrated on a second order process with a constraint on the input amplitude. © 2009 Elsevier Ltd. All rights reserved.

AB - A minimum variance performance map is introduced for constrained linear model predictive control (MPC). The minimum variance performance map provides a demonstration of the effect of constraints in an MPC on the best achievable controller performance. The constrained minimum variance controller is formulated for the MPC system to be monitored. Using multi-parametric quadratic programming (mp-QP), the linear, piecewise control law is obtained for the constrained minimum variance controller. The linear, piecewise control law is used with a Kalman filter to obtain the minimum output variance in each region of the state space partition. The minimum variance performance map is demonstrated on a second order process with a constraint on the input amplitude. © 2009 Elsevier Ltd. All rights reserved.

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Minimum variance performance map for constrained model predictive control

Abstract

Bibliographical note

Keywords

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