USING CMA-ES FOR TUNING COUPLED PID CONTROLLERS WITHIN MODELS OF COMBUSTION ENGINES
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F19%3A00374186" target="_blank" >RIV/68407700:21340/19:00374186 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.14311/NNW.2019.29.020" target="_blank" >https://doi.org/10.14311/NNW.2019.29.020</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.14311/NNW.2019.29.020" target="_blank" >10.14311/NNW.2019.29.020</a>
Alternative languages
Result language
angličtina
Original language name
USING CMA-ES FOR TUNING COUPLED PID CONTROLLERS WITHIN MODELS OF COMBUSTION ENGINES
Original language description
Proportional integral derivative (PID) controllers are important and widely used tools of system control. Tuning their gains is a laborious task, especially for complex systems such as combustion engines. To minimize the time an engineer spends tuning the gains in a simulation software, we propose to formulate a part of the problem as a black-box optimization task. In this paper, we summarize the properties and practical limitations of gain tuning in this particular application. We investigate the latest methods of black-box optimization and conclude that the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) with bi-population restart strategy, elitist parent selection and active covariance matrix adaptation is best suited for this task. Details of the algorithm's experiment-based calibration are explained as well as derivation of a suitable objective function. The method's performance is compared with that of PSO and SHADE. Finally, its usability is verified on six models of real engines.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10103 - Statistics and probability
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Neural Network World
ISSN
1210-0552
e-ISSN
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Volume of the periodical
29
Issue of the periodical within the volume
5
Country of publishing house
CZ - CZECH REPUBLIC
Number of pages
20
Pages from-to
325-344
UT code for WoS article
000497700600003
EID of the result in the Scopus database
2-s2.0-85077063705