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Digital
Library of the European Council for Modelling
and Simulation |
Title: |
Testing Hypotheses Used
In Analysis Of Control Quality |
Authors: |
Marek
Kubalcik, Tomas Barot |
Published in: |
(2019). ECMS 2019
Proceedings Edited by: Mauro Iacono, Francesco Palmieri, Marco Gribaudo,
Massimo Ficco, European Council for Modeling and Simulation. DOI: http://doi.org/10.7148/2019 ISSN:
2522-2422 (ONLINE) ISSN:
2522-2414 (PRINT) ISSN:
2522-2430 (CD-ROM) 33rd International ECMS Conference on
Modelling and Simulation,
Caserta, Italy, June 11th – June 14th, 2019 |
Citation
format: |
Marek Kubalcik, Tomas Barot (2019). Testing Hypotheses Used In Analysis Of Control Quality, ECMS 2019 Proceedings Edited by: Mauro Iacono, Francesco Palmieri, Marco Gribaudo, Massimo Ficco European Council for Modeling and Simulation. doi: 10.7148/2019-0132 |
DOI: |
https://doi.org/10.7148/2019-0132 |
Abstract: |
Simulation is
an important tool for testing and verification of newly designed or modified
control algorithms. One of the aims of the simulation verification is a
comparison of control quality achieved with new or modified methods with
control quality achieved with known methods. For an analysis of control
quality, criterions based namely on sum of powers of control errors and sum
of powers of control increments are commonly used. These criterions can
result only in descriptive attributes of control quality. It means that on
the basis of particular values of the criterions it is not possible to
identify if the control quality achieved with one algorithm is statistically
significantly different from control quality achieved with another algorithm.
The aim of this paper is examining of control quality with use of testing
hypotheses on existence of statistically significant differences between partial
values of the control quality criterions in individual sampling periods. The
analysis was performed on a strictly defined significance level 0.001, which
is a standardly used value in technical applications. A realization is
presented on a simulation of a multivariable predictive control with a
modified optimization technique. |
Full
text: |