Discriminating between disturbance and process model mismatch in model predictive control

Christopher A. HARRISON; S. Joe QIN

doi:10.1016/j.jprocont.2009.09.003

Discriminating between disturbance and process model mismatch in model predictive control

Christopher A. HARRISON, S. Joe QIN^*

^*Corresponding author for this work

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

50 Citations (Scopus)

Abstract

A novel method for discriminating faults in model predictive control is presented. The proposed method monitors the Kalman filter innovations to detect the presence of autocorrelation, which is an indication of suboptimal state estimation. The cause of the suboptimal state estimation is diagnosed by the observability of this innovations process. This task involves determining the order of the autocorrelation present in the innovations. The proposed MPC fault discrimination method is demonstrated on a SISO process and a MIMO process. © 2009 Elsevier Ltd. All rights reserved.

Original language	English
Pages (from-to)	1610-1616
Number of pages	7
Journal	Journal of Process Control
Volume	19
Issue number	10
Early online date	24 Oct 2009
DOIs	https://doi.org/10.1016/j.jprocont.2009.09.003
Publication status	Published - Dec 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 and members of the Texas–Wisconsin–California Control Consortium (TWCCC).

Keywords

Disturbance model validation
MPC performance monitoring
Model predictive control
Process model validation

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10.1016/j.jprocont.2009.09.003

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title = "Discriminating between disturbance and process model mismatch in model predictive control",

abstract = "A novel method for discriminating faults in model predictive control is presented. The proposed method monitors the Kalman filter innovations to detect the presence of autocorrelation, which is an indication of suboptimal state estimation. The cause of the suboptimal state estimation is diagnosed by the observability of this innovations process. This task involves determining the order of the autocorrelation present in the innovations. The proposed MPC fault discrimination method is demonstrated on a SISO process and a MIMO process. {\textcopyright} 2009 Elsevier Ltd. All rights reserved.",

keywords = "Disturbance model validation, MPC performance monitoring, Model predictive control, Process model validation",

author = "HARRISON, {Christopher A.} and QIN, {S. Joe}",

note = "This research was supported by a National Science Foundation Graduate Fellowship, a National Science Defense and Engineering Graduate Fellowship and members of the Texas–Wisconsin–California Control Consortium (TWCCC).",

year = "2009",

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language = "English",

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T1 - Discriminating between disturbance and process model mismatch in model predictive control

AU - HARRISON, Christopher A.

AU - QIN, S. Joe

N1 - This research was supported by a National Science Foundation Graduate Fellowship, a National Science Defense and Engineering Graduate Fellowship and members of the Texas–Wisconsin–California Control Consortium (TWCCC).

PY - 2009/12

Y1 - 2009/12

N2 - A novel method for discriminating faults in model predictive control is presented. The proposed method monitors the Kalman filter innovations to detect the presence of autocorrelation, which is an indication of suboptimal state estimation. The cause of the suboptimal state estimation is diagnosed by the observability of this innovations process. This task involves determining the order of the autocorrelation present in the innovations. The proposed MPC fault discrimination method is demonstrated on a SISO process and a MIMO process. © 2009 Elsevier Ltd. All rights reserved.

AB - A novel method for discriminating faults in model predictive control is presented. The proposed method monitors the Kalman filter innovations to detect the presence of autocorrelation, which is an indication of suboptimal state estimation. The cause of the suboptimal state estimation is diagnosed by the observability of this innovations process. This task involves determining the order of the autocorrelation present in the innovations. The proposed MPC fault discrimination method is demonstrated on a SISO process and a MIMO process. © 2009 Elsevier Ltd. All rights reserved.

KW - Disturbance model validation

KW - MPC performance monitoring

KW - Model predictive control

KW - Process model validation

UR - http://www.scopus.com/inward/record.url?scp=72149118719&partnerID=8YFLogxK

U2 - 10.1016/j.jprocont.2009.09.003

DO - 10.1016/j.jprocont.2009.09.003

M3 - Journal Article (refereed)

SN - 0959-1524

VL - 19

SP - 1610

EP - 1616

JO - Journal of Process Control

JF - Journal of Process Control

IS - 10

ER -

Discriminating between disturbance and process model mismatch in model predictive control

Abstract

Bibliographical note

Keywords

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