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Open AccessArticle

Performance Assessment of Predictive Control—A Survey

Institute of Control and Computation Engineering, Warsaw University of Technology, ul. Nowowiejska 15/19, 00-665 Warsaw, Poland
Algorithms 2020, 13(4), 97; https://doi.org/10.3390/a13040097
Received: 15 March 2020 / Revised: 8 April 2020 / Accepted: 10 April 2020 / Published: 17 April 2020
(This article belongs to the Special Issue Model Predictive Control: Algorithms and Applications)
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. View Full-Text
Keywords: advanced process control; model predictive control; control performance assessment; benchmark; model-free methods; model-based approach; DMC; GPC advanced process control; model predictive control; control performance assessment; benchmark; model-free methods; model-based approach; DMC; GPC
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Domański, P.D. Performance Assessment of Predictive Control—A Survey. Algorithms 2020, 13, 97.

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