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.
Bibliographical noteThis 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