A data-driven performance assessment approach for MPC systems under multiple operating conditions

Good performance of a controller in Model Predictive Control (MPC) system keeps the whole industrial process running well. Because of the complexity of the process, data-driven performance assessment approach, instead of model approach, becomes a popular topic. However, performance assessment is inaccurate when operating condition changes, because the performance benchmark should be different. This paper proposes an overall index to classify different operating conditions of real-time dataset. This index is the sum of two similarity factors by adding a weight value. One is the Principal Component Analysis (PCA) similarity factor and another is Bhattacharyya distance similarity factor. This index, considering both characteristic and spatial distance of datasets, identifies the operating condition that the real-time data belongs to. The effectiveness of this index is demonstrated in the case of simulation.