Detecting Model-Plant Mismatch without External Excitation

Any discrepancy between a process and the associated model used in control design will compromise closed-loop performance. In almost all current techniques to detect model-plant mismatch in model-based control systems there must be some sort of external excitation to overcome the effect of unmeasured disturbances on closed-loop signals. In this paper, we propose a novel technique that enables us to detect model-plant mismatch without introducing any external excitation. We show that model-plant mismatch in a closed loop system changes the cross-correlation coefficients between the model prediction error and the process input at certain lags. Indeed, by comparing the correlation between prediction error and input signals in the case of poor performance with that under good performance, one can detect model-plant mismatch. The results are illustrated on paper machine data.