Model predictive controller monitoring based on pattern classification and PCA

A pattern classification-based methodology is presented as a practical tool for monitoring model predictive control (MPC) systems. The principal component analysis (PCA) is used, especially PCA and distance similarity factors, to classify a window of current, MPC operating data into one of several classes. Pattern classifiers are developed using a comprehensive, simulated database of closed-loop MPC system behavior that includes a wide variety of disturbances and/or plant changes. The pattern classifiers can then be employed to classify current MPC performance by determining if the behavior is normal or abnormal, if an unusual plant disturbance is present, or if a significant plant change has occurred. The methodology is successfully applied in an extensive case study for the Wood-Berry distillation column model.