| Issue |
MATEC Web Conf.
Volume 292, 2019
23rd International Conference on Circuits, Systems, Communications and Computers (CSCC 2019)
|
|
|---|---|---|
| Article Number | 01037 | |
| Number of page(s) | 6 | |
| Section | Circuits and Systems | |
| DOI | https://doi.org/10.1051/matecconf/201929201037 | |
| Published online | 24 September 2019 | |
Statistical analysis of control quality of MPC using testing hypothesis
1 Department of Process Control, Tomas Bata University in Zlin, Faculty of Applied Informatics, Nad Stranemi 4511, 76005 Zlin, Czech Republic
2 Department of Mathematics with Didactics, University of Ostrava, Faculty of Education, Fr. Sramka 3, 70900 Ostrava, Czech Republic
* Corresponding author: kubalcik@utb.cz
Methods of the statistical induction have a significant role in the quantitative research. In a wide spectrum of research areas, the methods based on testing hypotheses have been frequently used. However, in the area of the process control, testing hypothesis has not been widely considered as an established tool for signal analyses, although signals in control loops are suitable for analysis by means of quantitative statistical methods due to their stochastic character. Particularly, a statistical paired comparison can be applied for analysis of control quality achieved with different control algorithms. This comparison can be based on a paired comparison of corresponding signals obtained with different or modified control algorithms. The aim of this paper is a proposal of incorporation of testing hypothesis to analysis of control quality. The analysis was performed on a strictly defined significance level 0.001, which is a standardly used value in technical applications. As an example was demonstrated analysis of control quality achieved with two versions of a predictive controller. Finally, achieved results of paired comparison using testing hypothesis are discussed.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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