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 PublicationsJournal Article (refereed)peer-review

50 Citations (Scopus)


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 languageEnglish
Pages (from-to)1610-1616
Number of pages7
JournalJournal of Process Control
Issue number10
Early online date24 Oct 2009
Publication statusPublished - Dec 2009
Externally publishedYes

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).


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


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