DOI 10.34229/KCA2522-9664.25.6.17

УДК 004.4’24, 004.3, 004.89, 004.942

В.М. ШИМКОВИЧ
Національний технічний університет України «Київський політехнічний інститут імені Ігоря Сікорського», Київ, Україна,  shymkovych.volodymyr@gmail.com

А.Ю. ДОРОШЕНКО
Інститут програмних систем НАН України; Національний технічний університет України «Київський політехнічний інститут імені Ігоря Сікорського», Київ, Україна,
 anatoliy.doroshenko@gmail.com

П.І. КРАВЕЦЬ
Національний технічний університет України «Київський політехнічний інститут імені Ігоря Сікорського», Київ, Україна,  peter_kravets@yahoo.com

О.А. ЯЦЕНКО
Інститут програмних систем НАН України, Київ, Україна,  oayat@ukr.net

МЕТОД АВТОМАТИЗОВАНОГО ПРОЄКТУВАННЯ
НЕЙРОМЕРЕЖЕВИХ КОНТРОЛЕРІВ НА ОСНОВІ ПЛІС

повний текст

СПИСОК ЛІТЕРАТУРИ

• 1. Jiang Y., Yang C., Na J., Li G., Li Y., Zhong J. A brief review of neural networks based learning and control and their applications for robots. Complexity. 2017. Vol. 2017. Article number 1895897. https://doi.org/10.1155/2017/1895897.
• 2. Shymkovych V., Telenyk S., Kravets P. Hardware implementation of radial-basis neural networks with Gaussian activation functions on FPGA. Neural Computing and Applications. 2021. Vol. 33, N 15. P. 9467–9479. https://doi.org/10.1007/s00521-021-05706-3.
• 3. Zhao X., Sun Y., Li Y., Jia N., Xu J. Applications of machine learning in real-time control systems: a review. Measurement Science and Technology. 2024. Vol. 36, N 1. Article ID 012003. https://doi.org/10.1088/1361-6501/ad8947.
• 4. Lopez D.M., Althoff M., Benet L. et al. ARCH-COMP22 category report: artificial intelligence and neural network control systems (AINNCS) for continuous and hybrid systems plants. Proc. of the 9th International Workshop on Applied Verification of Continuous and Hybrid Systems (ARCH22) (5 September 2022, Munich, Germany). 2022. Vol. 90. P. 142–184. https://doi.org/10.29007/wfgr.
• 5. Zhou H. A comprehensive review of artificial intelligence and machine learning in control theory. Applied and Computational Engineering. 2024. Vol. 116, N 1. P. 43–48. https://doi.org/10.54254/2755-2721/116/20251755.
• 6. Norgaard M., Ravn O., Poulsen N.K., Hansen L.K. Neural networks for modelling and control of dynamic systems. A practitioner’s handbook. London: Springer, 2000. 246 p.
• 7. He W., Gao H., Sun C. Dynamic modeling and neural network-based intelligent control of flexible systems. Hoboken, NJ: Wiley & Sons, 2025. 272 p.
• 8. Maki G.I., Hussain Z.M. Deep learning for control of digital systems. Journal of Physics: Conference Series. 2021. Vol. 1804, N 1. Article ID 012086. https://doi.org/10.1088/1742-6596/1804/1/012086.
• 9. Dev P., Jain S., Kumar Arora P., Kumar H. Machine learning and its impact on control systems: A review. Materials Today: Proceedings. Elsevier BV, 2021. Vol. 47. P. 3744–3749. https://doi.org/10.1016/j.matpr.2021.02.281.
• 10. Moe S., Rustad A.M., Hanssen K.G. Machine learning in control systems: An overview of the state of the art. In: Bramer M., Petridis M. (Eds.) Artificial Intelligence XXXV. SGAI 2018. Lecture Notes in Computer Science. Cham: Springer, 2018. Vol. 11311. P. 250–265. https://doi.org/10.1007/978-3-030-04191-5_23.
• 11. Omondi A.R., Rajapakse J.C. FPGA implementations of neural networks. New York: Springer, 2006. https://doi.org/10.1007/0-387-28487-7.
• 12. Feng G., Lozano R. Adaptive control systems. Oxford: Newnes, 1999. 352 p. https://doi.org/10.1016/B978-0-7506-3996-5.X5000-3.
• 13. Sigeru O., Marzuki B.K., Rubiyah Y. Neuro-control and its applications. London: Springer, 1996. 255 p. https://doi.org/10.1007/978-1-4471-3058-1.
• 14. Mohammadzaheri M., Chen L., Grainger S. A critical review of the most popular types of neuro control. Asian Journal of Control. 2011. Vol. 14, N 1. P. 1–11. https://doi.org/10.1002/asjc.449.
• 15. Ren Y.M., Alhajeri M.S., Luo J. et al. A tutorial review of neural network modeling approaches for model predictive control. Computers & Chemical Engineering. 2022. Vol. 165. Article ID 107956. https://doi.org/10.1016/j.compchemeng.2022.107956.
• 16. Norris G., Ducard G.J.J., Onder C. Neural networks for control: a tutorial and survey of stability-analysis methods, properties, and discussions. Proc. of the 2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME 2021) (7–8 October 2021, Flic en Flac, Mauritius). 2021. P. 1–6. https://doi.org/10.1109/iceccme52200.2021.9590912.
• 17. Zhang C., Ruan W., Xu P. Reachability analysis of neural network control systems. Proc. of the 37th AAAI Conference on Artificial Intelligence (7–14 February, 2023, Washington, USA). 2023. Vol. 37, N 12. P. 15287–15295. https://doi.org/10.1609/aaai.v37i12.26783.
• 18. Vo A.T., Truong T.N., Kang H.-J. Fixed-time RBFNN-based prescribed performance control for robot manipulators: achieving global convergence and control performance improvement. Mathematics. 2023. Vol. 11, N 10. Article ID 2307. https://doi.org/10.3390/math11102307.
• 19. Alanis A.Y., Rios J.D., Arana-Daniel N., Lopez-Franco C. Real-time neural control of all-terrain tracked robots with unknown dynamics and network communication delays. IngenierЗa InvestigaciЛn y TecnologЗa. Universidad Nacional Autonoma de Mexico. 2020. Vol. 21, N 3. P. 1–12. https://doi.org/10.22201/fi.25940732e.2020.21.3.026.
• 20. Chester D. Why two hidden layers are better then one. Proc. of the International Joint Conference on Neural Networks (IJCNN’90) (15–19 January 1990, Washington, USA). 1990. P. 265–268.
• 21. Kravets P. I., Shymkovych V. M., Samotyy V. Method and technology of synthesis of neural network models of object control with their hardware implementation on FPGA. Proc. of the 2017 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) (21–23 September 2017, Bucharest, Romania). 2017. P. 947–951. https://doi.org/10.1109/idaacs.2017.8095226.
• 22. Кравець П.І., Лукіна Т.Й., Шимкович В.М., Ткач І.І. Розробка та дослідження технології оцінювання показників нейромережевих моделей MIMO-об’єктів керування. Вісник НТУУ «КПІ». Інформатика, управління та обчислювальна техніка: Зб. наук. пр. 2012. № 57. С. 144–149.
• 23. Шимкович В.М., Дорошенко А.Ю., Мамедов Т.А., Яценко О.А. Автоматизоване проєктування штучного нейрона для програмованих логічних інтегральних схем на основі алгебро-алгоритмічного підходу. Міжнародний науково-технічний журнал «Проблеми керування та інформатики». 2022. Т. 67, № 5. С. 61–72. https://doi.org/10.34229/2786-6505-2022-5-6.
• 24. Doroshenko A., Yatsenko O. Formal and adaptive methods for automation of parallel programs construction: emerging research and opportunities. Hershey: IGI Global, 2021. 279 p. https://doi.org/10.4018/978-1-5225-9384-3.
• 25. LaMeres B.J. Introduction to logic circuits & logic design with VHDL. Cham: Springer, 2024. 539 p. https://doi.org/10.1007/978-3-031-42547-9.
• 26. Ross K., Snider R.K. Advanced digital system design using SoC FPGAs: An integrated hardware/software approach. Cham: Springer, 2024. 429 p. https://doi.org/10.1007/978-3-031-15416-4.
• 27. Goel K., Arun U., Sinha A. K. An efficient hardwired realization of embedded neural controller on System-On-Programmable-Chip (SOPC). International Journal of Engineering Research & Technology. 2014. Vol. 3, N 1. P. 276–284.
• 28. Singh S., Sanjeevi S., Suma V., Talashi A. FPGA implementation of a trained neural network. IOSR Journal of Electronics and Communication Engineering. 2015. Vol. 10, N 3. P. 45–54.
• 29. Samotyy V., Telenyk S., Kravets P., Shymkovych V., Posvistak T. A real time control system for balancing a ball on a platform with FPGA parallel implementation. Technical Transactions. 2018. Vol. 5. P. 109–117. https://doi.org/10.4467/2353737XCT.18.077.8559.
• 30. Symkovych V., Telenyk S., Kravets P., Posvistak T. FPGA Implementation of the PID algorithm for real time ball balancing on the platform. Proc. of the Fourth International Conference on “Automatic Control and Information Technology” (14–16 December 2017, Krakow, Poland). 2017. P. 160–169.
• 31. Doroshenko A., Shymkovych V., Yatsenko O., Mamedov T. Automated software design for FPGAs on an example of developing a genetic algorithm. Proc. of the 17th International Conference on ICT in Education, Research and Industrial Applications. Integration, Harmonization and Knowledge Transfer (ICTERI 2021) (28 September – 2 October 2021, Kherson, Ukraine). CEUR Workshop Proceedings. 2021. Vol. 3013. P. 74–85. https://ceur-ws.org/Vol-3013/20210074.pdf.
• 32. Mohammadi A., Ryu J.-C. Neural network-based PID compensation for nonlinear systems: ball-on-plate example. International Journal of Dynamics and Control. 2020. Vol. 8. P. 178–188. http://doi.org/10.1007/s40435-018-0480-5.
• 33. Shaheer M., Hashmi H., Khan S. et al. Control of a ball-bot using a PSO trained neural network. Proc. of the 2nd International Conference on Control, Automation and Robotics (ICCAR 2016) (28–30 April 2016, Hong Kong, China). New York: IEEE, 2016. P. 24–28. http://doi.org/10.1109/ICCAR.2016.7486692.