Peer-reviewed articles 17,970 +


Marta Blahova
•    Prof. DSc. Oleksandr Trofymchuk, UKRAINE 
•    Prof. Dr. hab. oec. Baiba Rivza, LATVIA
Predictive process control is a method of regulation suitable for controlling various types of systems, which is based on the idea of using the prediction of future system behavior and its optimization. Normally, a system model is used to predict behavior, and therefore it is necessary for the correct function of predictive control to make its correct selection and determine its parameters so that the controlled system is described as accurately as possible. Another advantage of predictive control is the possibility of including signal restrictions directly in the controller. The result is the application of some elements of artificial intelligence in suitable areas of predictive control, especially the use of simple evolutionary algorithms in optimization and neural networks as nonlinear models. One of the chapters of the article describes the possibilities of using these elements. It is proved that in addition to classical optimization algorithms, it is also possible to use simple evolutionary algorithms for optimization of prediction, while the computational complexity can be comparable depending on the type of solved problem and settings. The article describes a suitable selection of model systems with slow dynamics, their derivation, and the creation of nonlinear models in the form of scalable neural networks. The potential advantage of this approach for the control of systems that are difficult to describe or for the control of systems whose mathematicalphysical description is not known. The chapter of the article also deals with the possibility of using the found models on real systems and determining the necessary conditions and requirements for their application.
[1] Kluever, Craig A. (2015). Dynamic systems: modeling, simulation, and control. Hoboken, NJ: John Wiley and Sons, Inc. ISBN 9781118289457.
[2] Rossiter, J. (2003). Model-based Predictive Control: A Practical Approach. CRC Press.
[3] Khaled, N.; Bibin Pattel. (2018). Practical design and application of model predictive control: MPC for MATLAB and Simulink users. Kidlington, Oxford: Butterworth-Heinemann, an imprint of Elsevier. ISBN 9780128139196.
[4] Rawlings, J. B.; David Q. Mayne. (2009). Model predictive control: theory and design. Madison, Wis.: Nob Hill Pub. 533 s. ISBN 9780975937709.
[5] Mikles, J.; Fikar M. (2004). Modeling, identification and process control 2. Identification and optimal control. STU Press, Bratislava. 260 pp. ISBN 80-227-2134-4.
[6] Bobal, V. (2008). Adaptive and predictive control. Edition 1. Zlin: Tomas Bata University in Zlin, 134 pp. ISBN 978-80-7318-662-3.
[7] Ploskas, N.; Nikolaos Samaras. (2017). Linear programming using MATLAB. Cham: Springer. ISBN 9783319659190.
[8] Kochendefer, M. J. and Tim A. Wheeler. (2019). Algorithms for optimization. ISBN 9780262039420.
[9] Wang, L. (2009). Model predictive control system design and implementation using MATLAB. London: Springer. 375 s. ISBN 9781848823303.
[10] Lee Gue Myung, Tam N.N., Yen Nquyen Dong. (2005). Quadratic Programming and Affine Variational Inequalities: A Qualitative Study. Springer US. ISBN 978-0- 387-24278-1.
[11] Back, T.; B. Fogel David; Michalewicz Zbigniew. (1997). Handbook of Evolutionary Computation. Oxford University Press. 988 pp. ISBN 0750303927.
[12] Mirjalili, S. (2018). Evolutionary algorithms and neural networks: theory and applications. ISBN 9783319930244; 1860-949X.
[13] Simon, D. (2013). Evolutionary optimization algorithms: Biologically-Inspired and population-based approaches to computer intelligence. Hoboken, New Jersey: John Wiley & Sons Inc. ISBN 9780470937419.
[14] Anonymous. (2018). Selecting a Model Structure in the System Identification Process. Available from:
[15] Russell, S. J.; Peter Norvig. (2010). Artificial intelligence: a modern approach. Upper Saddle River, N.J.: Prentice Hall. 1132 pp. ISBN 0132071487.
This research was based on the support of the Internal Grant Agency of Tomas Bata University in Zlin, the IGA / FAI / 2022/002 project and the Department of Security Engineering, Faculty of Applied Informatics.
Proceedings of 22nd International Multidisciplinary Scientific GeoConference SGEM 2022
22nd International Multidisciplinary Scientific GeoConference SGEM 2022, 04 - 10 July, 2022
Proceedings Paper
STEF92 Technology
International Multidisciplinary Scientific GeoConference SGEM
SWS Scholarly Society; Acad Sci Czech Republ; Latvian Acad Sci; Polish Acad Sci; Serbian Acad Sci and Arts; Natl Acad Sci Ukraine; Natl Acad Sci Armenia; Sci Council Japan; European Acad Sci, Arts and Letters; Acad Fine Arts Zagreb Croatia; Croatian Acad Sci and Arts; Acad Sci Moldova; Montenegrin Acad Sci and Arts; Georgian Acad Sci; Acad Fine Arts and Design Bratislava; Turkish Acad Sci.
04 - 10 July, 2022
Predictive control; discrete dynamic models; artificial intelligence; neural networks; evolutionary algorithms

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