The paper studies the problem of optimization of real-time control systems described within the actor model. The optimization problem is formulated as a problem of optimal configuration of the control cycle, i.e., distribution of functional elements-actors by groups, flows and execution sequence. We propose a configuration algorithm, which, although it does not reduce the number of analyzed configuration variants, reduces the amount of calculations for each of the variants. In addition to the optimization variants with a limit on the total cycle time and with a limit on the control system resources considered in the authors' previous works, the paper considers the problem of reducing the number of input and output ports through which the element-actors exchange data. The research shows that the number of ports can be reduced without compromising the functionality of the control system. This is due to the sequential nature of element-actors execution within one group of one flow. As a result, the same input or output ports can be used to communicate an actor element with several others. In addition to matching different control loop configurations, the problem of reducing the number of ports can also be solved by using shared memory for element-actor communication. When the control system is built according to memory-oriented architecture, small amounts of data are transferred through high-speed shared memory, which reduces the acuteness of the problem of queue formation.
1. Krasilnikyants E.V., Burkov A.P., Ivankov V.A., Buldukyan G.A., Elnikovskij V.V., Varkov A.A. Technological objects traffic control systems. Vestnik of Ivanovo State Power Engineering University. 2007;(4):42–46. (In Russ.).
2. Merkuriev I.V., Komerzan E.V., Sviridenko O.V., Labahua L.R. Methods to improve the speed and accuracy of navigation systems and motion control of automatic robotic tools. Sistemnye tekhnologii. 2018;(3):99–104. (In Russ.).
3. Zelenskii A.A., Gribkov A.A. Actor modeling of real-time cognitive systems: ontological basis and software-mathematical implementation. Philosophical Thought. 2024;(1):1–12. (In Russ.). https://doi.org/10.25136/2409-8728.2024.1.69254
4. Burgin M. Systems, Actors and Agents: Operation in a multicomponent environment. arXiv. URL: https://arxiv.org/abs/1711.08319 [Accessed 3rd March 2025].
5. Zelenskii A.A., Gribkov A.A. Configuration of memory-oriented motion control system. Software systems and computational methods. 2024;(3):12–25. (In Russ.). https://doi.org/10.7256/2454-0714.2024.3.71073
6. Knuth D.E. The Art of Computer Programming. Volume 3. Sorting and Searching. Second Edition. Addison Wesley Longman; 1998. 780 p.
7. Kalyaev I., Zaborovskii V. Iskusstvennyi intellekt: ot metafory k tekhnicheskim resheniyam. Control Engineering Russia. 2019;(5):26–31. (In Russ.).
8. Mutlu O. Memory-Centric Computing. arXiv. URL: https://doi.org/10.48550/arXiv.2305.20000 [Accessed 3rd March 2025].
9. Ke L., Zhang X., So J., et al. Near-Memory Processing in Action: Accelerating Personalized Recommendation with AxDIMM. IEEE Micro. 2022;42(1):116–127. https://doi.org/10.1109/MM.2021.3097700
10. Suh S.-H., Kang S.K., Chung D.-H., Stroud I. Theory and Design of CNC Systems. London: Springer; 2008. 456 p. https://doi.org/10.1007/978-1-84800-336-1
Zekenskii Aleksandr Aleksandrovich
PhD in Technical Science, docent
WoS | Scopus | ORCID | eLibrary |
Scientific and Production Complex "Technological Center"
Moscow, Russia
Gribkov Andrey Armovich
Doctor of Technical Science
WoS | Scopus | ORCID | eLibrary |
Scientific and Production Complex "Technological Center"
Moscow, Russia