Detection and Location of Model-Plant Mismatch in Multiple Input Multiple Output Systems under Model Predictive Controller Using Granger Causality Method

Abstract

In closed-loop control systems, the model accuracy exerts large influences on the controllability, stability and quality of the whole process. Among all the faults that could affect the system performance, Model Plant Mismatch (MPM) is the one that not only directly threatens the system stability but also deteriorates the controller performance. Meanwhile, MPM has a major influence on the qualities of outputs about industrial products. In this work, a new detection method based on Granger Causality is proposed to detect and locate the MPM in multiple input multiple output systems. Causality can reflect the relations between the mismatch fault and its negative effects on model predictive control(MPC) systems. With the assistance of disturbance transfer function models, the causality method can further be used to locate the mismatch positions and get the correct channels of each kind of mismatches. The proposed method was examined and validated in the Wood-Berry process in contrast to the decussation location method under model predictive controller.

Employee Performance Assessment Model in Survey Service Companies Using Analytical Hierarchy Process and Naive Bayes

“…The model that has been built requires an examination stage to match the real state of the object of research, namely by verifying the model (Yin & McKay, 2018). The process to ascertain whether the framework prepared for model design is correct and significantly assists the research objectives is called model validation (Sanhueza & Nikulin, 2019;Chen et al, 2021). From the description above, this study will use the AHP and Naïve Bayes methods in obtaining variable weights so that the employee performance assessment model becomes more effective.…”