Mechatronic Module with Alternative-Probability Control

Authors

DOI:

https://doi.org/10.20535/2521-1943.2025.9.4(107).345026

Keywords:

mechatronic module, automation, alternative probabilistic control, adaptive algorithms, system operating modes

Abstract

This article presents control algorithms for adaptive mechanical systems. Object of research: processes occurring in industrial hydraulic drive systems during their operation. Subject of research: dependence of operational efficiency of industrial hydraulic drive systems on the term and operating modes, and applied fundamental circuit solutions and technical means that affect the functional, energy, and cost indicators of the system.The problems solved in the presented article are actual tasks. They involve the use of previous experience in the operation of a specific mechatronic system in specific conditions, for the formation of an adaptive control algorithm. The study is based on the creation of logical interpretations in the algorithm for decision-making. The simultaneous consideration of logical connections and the probability of the component “success of system actions” is considered. On the basis of which a two-component structure of logical expressions of control commands is formed. Accordingly, examples are given for evaluating each of the operation options. The article presents the scope and conditions of practical use of the obtained results. They are based on several examples, namely, the technical implementation of the manipulator macromodule with alternative probabilistic control is considered. It is possible to use the success of the system’s actions and the choice of the option for distributing attempts by digits or/and the basis of logarithmic weight. The peculiarity of the obtained results lies in the use of the criterion of “volume of involved memory” in the control system. This makes it possible to prioritize one of the alternative reactions of the system to external excitation, which provides a basis for the formation of control commands.

References

  1. A. Rojko, “Industry 4.0 Concept: Background and Overview”, International Journal of Interactive Mobile Technologies, vol. 11, no. 5, pp. 77–90, 2017. DOI: https://doi.org/10.3991/ijim.v11i5.7072.
    |
  2. Y. Burennikov, L. Kozlov, Y. Shevchuk and V. Pyliavets, “Mechatronic Hydraulic System with Adaptive Controler on the Basis of Neural Networks”, Buletinul Universitatea Tehnică „Gheorghe Asachi” din Iaşi, vol. LXI (LXV), no. 1-2, pp. 151-159, 2015.
  3. J. Lee, H.-A. Kao and S. Yang, “Service Innovation and Smart Analytics for Industry 4.0 and Big Data Environment", Procedia CIRP, vol. 16, pp. 3–8, 2014. DOI: https://doi.org/10.1016/j.procir.2014.02.001.
    |
  4. Y. Li, H. Wang and J. Zhang, “Precise control of hydraulic actuators using adaptive backstepping and neural networks”, Computers, Materials & Continua, vol. 141, no. 2, pp. 1235–1250, 2024.
  5. L. Knapčíková and M. Balog (eds.), Industry 4.0: Trends in Management of Intellegent Manufacturing Systems. Cham: Springer, Cham, 2019, 146 p. DOI: https://doi.org/10.1007/978-3-030-14011-3.
  6. M. Brettel, N. Friederichsen, M. Keller and M. Rosenberg, “How Virtualization, Decentralization and Network Building Change the Manufacturing Landscape: An Industry 4.0 Perspective”, International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering, vol. 8, no. 1, рр. 37–44, 2014. DOI: https://doi.org/10.5281/zenodo.1336426.
  7. L. Schmidt and K. V. Hansen, “Electro-Hydraulic Variable-Speed Drive Networks - Idea, Perspectives, and Energy Saving Potentials”, Energies, vol. 15, no. 3, p. 1228, 2022. DOI: https://doi.org/10.3390/en15031228.
    |
  8. T. Bauernhansl, M. ten Hompel and B. Vogel-Heuser, Industrie 4.0 in Produktion, Automatisierung und Logistik: Anwendung, Technologien, Migration. Wiesbaden: Springer Vieweg, 2014, 648 p. DOI: https://doi.org/10.1007/978-3-658-04682-8.
  9. S.-C. Vanegas-Ayala, J. Barón-Velandia and D.-D. Leal-Lara, “A Systematic Review of Greenhouse Humidity Prediction and Control Models Using Fuzzy Inference Systems”, Advances in Human-Computer Interaction, vol. 2022, p. 8483003, 2022. DOI: https://doi.org/10.1155/2022/8483003.
    |
  10. S. Zhao, K. Chen, X. Zhang, Y. Zhao, G. Jing, C. Yin and X. Xiao, “A High-Order Load Model and the Control Algorithm for an Aerospace Electro-Hydraulic Actuator”, Actuators, vol. 10, no. 3, p. 53, 2021. DOI: https://doi.org/10.3390/act10030053.
    |
  11. P. K. Anokhin, Biologiya i neyrofiziologiya uslovnogo refleksa. Moscow: Meditsina, 1968, 548 p.
  12. K. Belikov and O. Gubarev, “Adaptation of control in electropneumatic systems with discrete software control”, Bulletin of the National Technical University "KhPI". Series: Hydraulic machines and hydraulic units, no. 1, pp. 18–22, 2020. DOI: https://doi.org/10.20998/2411-3441.2020.1.03.
  13. O. S. Ganpantsurova and O. P. Gubarev, "Lohiko-inertsiyna skladova komand keruvannya vykonavchym modulem mekhatronnoyi systemy", in Materialy XVII Mizhnarodnoyi naukovo-tekhnichnoyi konferentsiyi AS PGP "Promyslova hidravlika i pnevmatyka", Kharkiv, Ukraine, 2016.
  14. V. M. Glushkov, Yu. V. Kapitonova and A. T. Mishchenko, Logicheskoye proyektirovaniye diskretnykh ustroystv. Kyiv: Naukova dumka, 1987, 264 p.
  15. A. P. Gubarev, Diskretno-logicheskoye upravleniye v sistemakh gidropnevmoavtomatiki. Kyiv: ISMO, 1997, 224 p.
  16. A. P. Gubarev, K voprosu adaptatsii logicheskogo upravleniya. Deponent UkrNIINTI, no. 282-Uk 86, 1986, 29 p.
  17. L. G. Kozlov, “Using neural network for regulation time reduction in the mechatronic hydraulic system”, Visnyk Sumskoho derzhavnoho universytetu. Seriya "Tekhnichni nauky", no. 4, pp. 165–174, 2013. Available: https://essuir.sumdu.edu.ua/handle/123456789/33794.
  18. T. Petrakis, A. Kavga, V. Thomopoulos and A. A. Argiriou, “Neural Network Model for Greenhouse Microclimate Predictions”, Agriculture, vol. 12, no. 6, p. 780, 2022. DOI: https://doi.org/10.3390/agriculture12060780.
    |
  19. V. P. Tarasik and S. A. Rynkevich, Intellektualnyye sistemy upravleniya avtotransportnymi sredstvami. Minsk: UP "Tekhnoprint", 2004, 512 p.
  20. M. V. Cherkashenko, Avtomatizatsiya proyektirovaniya sistem gidro- i pnevmoprivodov s diskretnym upravleniyem. Kharkov: NTU "KhPI", 2007, 210 p.

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Published

2025-12-29

How to Cite

[1]
O. Gubarev, K. Belikov, and A. Murashchenko, “Mechatronic Module with Alternative-Probability Control”, Mech. Adv. Technol., vol. 9, no. 4(107), pp. 432–441, Dec. 2025.

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Section

Mechanics