Performance Assessment of Predictive Control—A Survey

Paweł Domański


Model Predictive Control constitutes an important element of any modern control system. There is growing interest in this technology. More and more advanced predictive structures have been implemented. The first applications were in chemical engineering, and now Model Predictive Control can be found in almost all kinds of applications, from the process industry to embedded control systems or for autonomous objects. Currently, each implementation of a control system requires strict financial justification. Application engineers need tools to measure and quantify the quality of the control and the potential for improvement that may be achieved by retrofitting control systems. Furthermore, a successful implementation of predictive control must conform to prior estimations not only during commissioning, but also during regular daily operations. The system must sustain the quality of control performance. The assessment of Model Predictive Control requires a suitable, often specific, methodology and comparative indicators. These demands establish the rationale of this survey. Therefore, the paper collects and summarizes control performance assessment methods specifically designed for and utilized in predictive control. These observations present the picture of the assessment technology. Further generalization leads to the formulation of a control assessment procedure to support control application engineers.
Author Paweł Domański (FEIT / AK)
Paweł Domański,,
- The Institute of Control and Computation Engineering
Journal seriesAlgorithms, ISSN 1999-4893
Issue year2020
Publication size in sheets4.85
Article number97
Keywords in Englishadvanced process control; model predictive control; control performance assessment; benchmark; model-free methods; model-based approach; DMC; GPC
ASJC Classification1703 Computational Theory and Mathematics; 2605 Computational Mathematics; 2612 Numerical Analysis; 2614 Theoretical Computer Science
Languageen angielski
DomanskiP algorithms-13-00097.pdf 594.11 KB
Score (nominal)40
Score sourcejournalList
ScoreMinisterial score = 40.0, 17-06-2020, ArticleFromJournal
Publication indicators Scopus Citations = 0; Scopus SNIP (Source Normalised Impact per Paper): 2018 = 0.687
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