The concept of an automated control system
News of the Kabardin-Balkar scientific center of RAS, Tome 26 (2024) no. 5, pp. 13-28.

Voir la notice de l'article provenant de la source Math-Net.Ru

This article presents the concept of an automated control system for the production process of robotic complexes. The diagram of the control system for the production process of robotic complexes and the structure of the interaction of agents in the described production model are shown. It is assumed that AI based on multi-agent neurocognitive architectures will be used as an intelligent decision-making system in the control system. Such a model will make it possible to simulate complex processes of interaction both between organizational nodes and between external actors. In the future, the system will be able to provide adequate planning at the organizational level, taking into account all available factors.
Keywords: robotics, intelligent systems, multiagent algorithms, automated control systems
Mots-clés : production
@article{IZKAB_2024_26_5_a0,
     author = {K. Ch. Bzhikhatlov and A. D. Kravchenko},
     title = {The concept of an automated control system},
     journal = {News of the Kabardin-Balkar scientific center of RAS},
     pages = {13--28},
     publisher = {mathdoc},
     volume = {26},
     number = {5},
     year = {2024},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/IZKAB_2024_26_5_a0/}
}
TY  - JOUR
AU  - K. Ch. Bzhikhatlov
AU  - A. D. Kravchenko
TI  - The concept of an automated control system
JO  - News of the Kabardin-Balkar scientific center of RAS
PY  - 2024
SP  - 13
EP  - 28
VL  - 26
IS  - 5
PB  - mathdoc
UR  - http://geodesic.mathdoc.fr/item/IZKAB_2024_26_5_a0/
LA  - ru
ID  - IZKAB_2024_26_5_a0
ER  - 
%0 Journal Article
%A K. Ch. Bzhikhatlov
%A A. D. Kravchenko
%T The concept of an automated control system
%J News of the Kabardin-Balkar scientific center of RAS
%D 2024
%P 13-28
%V 26
%N 5
%I mathdoc
%U http://geodesic.mathdoc.fr/item/IZKAB_2024_26_5_a0/
%G ru
%F IZKAB_2024_26_5_a0
K. Ch. Bzhikhatlov; A. D. Kravchenko. The concept of an automated control system. News of the Kabardin-Balkar scientific center of RAS, Tome 26 (2024) no. 5, pp. 13-28. http://geodesic.mathdoc.fr/item/IZKAB_2024_26_5_a0/

[1] The new high-tech strategy innovations for Germany https://ec.europa.eu/futurium/en

[2] G. Erboz, “Managerial trends in the development of enterprises in globalization era”, How to define Industry 4.0: Main pillars of Industry 4.0, 2017, 761–767, Slovakia

[3] R. Badarinath, V. V. Prabhu, “Advances in Internet of Things (IoT) in manufacturing”, Advances in production management systems. The Path to intelligent, collaborative and sustainable manufacturing, IFIP Advances in Information and Communication Technology, v. 513, eds. Lodding, H., Riedel, R., Thoben, KD., von Cieminski, G., Kiritsis, D.https://ec.europa.eu/futurium/en, 2017, 111–118 pp. | DOI

[4] H. Elhoone, T. Zhang, M. Anwar, S. Desai, “Cyber-based design for additive manufacturing using artificial neural networks for Industry 4.0”, International Journal of Production Research, 58:9 (2019), 2841–2861 | DOI

[5] J. M. Fordal, P. Schjolberg, H. Helgetun et al., “Application of sensor data based predictive maintenance and artificial neural networks to enable Industry 4.0”, Advances in Manufacturing, 11:2 (2023), 248–263 | DOI

[6] A. I.M. Schwebig, R. Tutsch, “Compilation of training datasets for use of convolutional neural networks supporting automatic inspection processes in industry 4.0 based electronic manufacturing”, Journal of Sensors and Sensor Systems, 9:1 (2020), 167–178 | DOI

[7] D. O. Sanz, C. Q. Gomez Munoz, F. P. Garcia Marquez, “Convolutional neural networks as a quality control in 4.0 industry for screws and nuts”, Lecture Notes in Networks and Systems, 2022, 13–29 | DOI

[8] T. Mujber, T. Szecsi, M. Hashmi, “Virtual reality applications in manufacturing process simulation”, Journal of Materials Processing Technology, 155-156 (2004), 1834–1838 | DOI

[9] N. Gramegna, E. D. Corte, S. Poles, “Manufacturing process simulation for product design chain optimization”, Materials and Manufacturing Processes, 26:3 (2011), 527–533 | DOI

[10] A. Smirnov, N. Shilov, M. Shchekotov, “Ontology-Based modelling of state machines for production robots in smart manufacturing systems”, International Journal of Embedded and Real-Time Communication Systems, 11:2 (2020), 76–91 | DOI | MR

[11] S. B. Yeom, E. S. Ha, M. S. Kim et al., “Application of the discrete element method for manufacturing process simulation in the pharmaceutical industry”, Pharmaceutics, 11:8 (2019), 414 | DOI

[12] K. Ch. Bzhikhatlov, I. A. Pshenokova, “Intelligent spraying system of autonomous mobile agricultural robot”, Smart Innovation, Systems and Technologies, 2023, 269–278 | DOI

[13] K. Bzhikhatlov, A. Zammoev, L. Kokova, I. Pshenokova, “Autonomous robot for monitoring ground archaeological sites”, Izvestiya SFedU Engineering sciences, 1 (2023), 100–109 | DOI

[14] Z. Nagoev, I. Pshenokova, O. Nagoeva et al., “Learning algorithm for an intelligent decision making system based on multi-agent neurocognitive architectures”, Cognitive Systems Research, 66 (2021), 82–88 | DOI

[15] M. I. Anchekov, A. Z. Apshev, K. Ch. Bzhikhatlov et al., “Formal genome model of a general artificial intelligence agent based on multi-agent neurocognitive architectures”, News of the Kabardino-Balkarian Scientific Center of RAS, 2023, no. 5 (115), 11–24 | DOI