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Existing model-plant mismatch detection and isolation methods mainly employ correlation analysis approaches to detect the sub-model that have statistically significant mismatch from the plant. However, statistical significance does not ensure that any control performance deterioration is due to the detected mismatch. A small but statistically significant mismatch may not have any impact on the performance of the controller. This paper presents the effect of model plant mismatch (MPM) on the performance of model predictive controller (MPC) and a systematic approach to determine the thresholds of mismatches above which the performance deterioration can be considered significant. A simulation case study of the Wood and Berry distillation model with MPC is used as a case study to demonstrate the efficacy of the proposed approach. For the stated case study, a 70% increase in the overall integral error (OIE) for set-point tracking problem is found to be an acceptable limit for the MPC performance deterioration. The thresholds of gain, time delay and time constant MPM for 70% increase in OIE for Wood and Berry simulation case study are found to be {(-20% +40%) (-13% +7%); (-40% +31%) (-7% +27%)}, {(-40% +40%) (-40% +22%); (-20% +17%) (-29% +27%)} and {(-40% +15%) (-20% +40%); (-23% +40%) (-28% +36%)}, respectively

Effect of Model Plant Mismatch on MPC Performance and Mismatch Threshold Determination