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Perspectives and challenges in performance assessment of model predictive control
Author(s) -
Botelho Viviane,
Trierweiler Jorge Otávio,
Farenzena Marcelo,
Duraiski Ricardo
Publication year - 2016
Publication title -
the canadian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.404
H-Index - 67
eISSN - 1939-019X
pISSN - 0008-4034
DOI - 10.1002/cjce.22500
Subject(s) - setpoint , benchmark (surveying) , model predictive control , process (computing) , computer science , task (project management) , reliability engineering , control (management) , process control , engineering , systems engineering , artificial intelligence , geodesy , geography , operating system
The longevity of each MPC application is strongly related to its performance maintenance. This work provides an overview of the methodologies available to fulfill this task, including a discussion about some special requirements of performance assessment methodologies for typical industrial MPC applications. The available methodologies were compared using these requirements. The best approaches were selected and compared to a new method proposed by the authors. These techniques have been applied in two case studies: the Shell benchmark process and the quadruple‐tank process. The results show that the control policy (setpoint, soft constraints, targets) followed in the MPC application should be the determining factor in selecting the methodology for performance assessment.