Methodology for Detecting Model-Plant Mismatches Affecting Model Predictive Control Performance

The model quality for a model predictive control (MPG) is critical for the control loop performance. Thus, assessing the effect of model plant mismatch (MPM) is fundamental for performance assessment and monitoring the MPG. This paper proposes a method for evaluating model quality based on the investigation of closed-loop data and the nominal output sensitivity function, which facilitates the assessment procedure for the actual closed-loop performances. The effectiveness of the proposed method is illustrated by a multivariable case study, considering linear and nonlinear plants.