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Model Predictive Evolutionary Temperature Control via Neural-Network-Based Digital Twins

Ates, Cihan ORCID iD icon 1; Bicat, Dogan 1; Yankov, Radoslav 2; Arweiler, Joel 1; Koch, Rainer 1; Bauer, Hans-Jörg 1
1 Institut für Thermische Strömungsmaschinen (ITS), Karlsruher Institut für Technologie (KIT)
2 Karlsruher Institut für Technologie (KIT)

Abstract:

In this study, we propose a population-based, data-driven intelligent controller that leverages neural-network-based digital twins for hypothesis testing. Initially, a diverse set of control laws is generated using genetic programming with the digital twin of the system, facilitating a robust response to unknown disturbances. During inference, the trained digital twin is utilized to virtually test alternative control actions for a multi-objective optimization task associated with each control action. Subsequently, the best policy is applied to the system. To evaluate the proposed model predictive control pipeline, experiments are conducted on a multi-mode heat transfer test rig. The objective is to achieve homogeneous cooling over the surface, minimizing the occurrence of hot spots and energy consumption. The measured variable vector comprises high dimensional infrared camera measurements arranged as a sequence (655,360 inputs), while the control variable includes power settings for fans responsible for convective cooling (3 outputs). Disturbances are induced by randomly altering the local heat loads. The findings reveal that by utilizing an evolutionary algorithm on measured data, a population of control laws can be effectively learned in the virtual space. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000161983
Veröffentlicht am 06.09.2023
Originalveröffentlichung
DOI: 10.3390/a16080387
Scopus
Zitationen: 1
Dimensions
Zitationen: 1
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Thermische Strömungsmaschinen (ITS)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 08.2023
Sprache Englisch
Identifikator ISSN: 1999-4893
KITopen-ID: 1000161983
Erschienen in Algorithms
Verlag MDPI
Band 16
Heft 8
Seiten Art.-Nr.: 387
Bemerkung zur Veröffentlichung Gefördert durch den KIT-Publikationsfonds
Vorab online veröffentlicht am 12.08.2023
Schlagwörter model predictive control, digital twin, neural network, deep learning, genetic programming, evolutionary algorithm, heat transfer, temperature control, data driven control, data-driven engineering
Nachgewiesen in Scopus
Dimensions
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