D.Y. Openkin1, S.V. Chernomordov2
1,2 Yelets Bunin Yelets State University (Yelets, Russia)
The development of instrumental and methodological support for modeling nonlinear control systems with switching is an urgent problem. Various modifications of numerical optimization methods and artificial intelligence technologies are used to solve this problem. The purpose of this article is to develop algorithmic software for modeling controlled switching systems based on the use of intelligent technologies and numerical optimization methods. Algorithmic software for the synthesis of feedback controls using a PID controller is developed. Intelligent technologies for modeling controlled switching systems are characterized. An algorithm using global parametric optimization methods is proposed for tuning the PID controller. Models of nonlinear dynamic switching systems are studied. An algorithm for finding optimal trajectories based on neural network automata is proposed. The basis for further research is developed, in which it is planned to create a software implementation of the switching algorithm and the neural network algorithm. The practical significance of the results is that the developed algorithmic software will allow analyzing the influence of various parameters on the quality and speed of functioning of intelligent control switching systems. The obtained results can be used in various problems of modeling and global optimization of controlled systems: technical systems with switching modes of operation, transport systems, as well as in problems of neural network modeling and machine learning.
Openkin D.Y., Chernomordov S.V. Application of intelligent technologies for modeling of controlled switching systems. Science Intensive Technologies. 2021. V. 22. № 4. P. 26−33. DOI: https://doi.org/10.18127/j19998465-202104-04 (in Russian)
- Sinicyn I.N., Druzhinina O.V., Belousov V.V., Masina O.N., Petrov A.A. Opyt razrabotki instrumental'no-metodicheskogo obespecheniya dlya resheniya zadach modelirovaniya upravlyaemyh dinamicheskih sistem s primeneniem tekhnologij mashinnogo obuche-niya i otechestvennyh programmno-apparatnyh sredstv. Nelinejnyj mir. 2019. T. 17. № 4. S. 5–19 (in Russian).
- Shpilevaya O.Ya., Kotov K.Yu. Pereklyuchaemye sistemy: ustojchivost' i proektirovanie (obzor). Avtometriya. 2008. T. 44. № 5. S. 71–87 (in Russian).
- Druzhinina O.V., Masina O.N., Petrov A.A. Models for the control of technical systems motion taking into account optimality conditions. Proceedings of the VIII International Conference on Optimization Methods and Applications «Optimization and Applications» (OPTIMA– 2017), Petrovac, Montenegro, October 2–October 7, 2017. Published at http://CEUR-WS.org 10.11.2017. Vol. 1987. P. 386–391.
- Druzhinina O.V., Masina O.N., Petrov A.A. The synthesis of the switching systems optimal parameters search algorithms. Communications in Computer and Information Science. 2019. T. 974. S. 306–320.
- Hajkin S. Nejronnye seti. M.: Izdatel'skij dom «Vil'yams», 2006.
- Kruglov V.V., Dli M.N., Golunov R.Yu. Nechetkaya logika i iskusstvennye nejronnye seti. M.: Fizmatlit. 2001 (in Russian).
- Plotnikova N.P., Fedosin S.A., Teslya V.V. Gravitation search training algorithm for asynchronous distributed multilayer perceptron model. Lecture Notes in Electrical Engineering. 2015. V. 312. P. 417–423.
- Karpenko A.P. Sovremennye algoritmy poiskovoj optimizacii. Algoritmy, vdohnovlennye prirodoj. Izd. 2-e. M.: MGTU im. N.E. Baumana. 2016 (in Russian).
- Posypkin M.A. Parallel'nyj evristicheskij algoritm global'noj optimizacii. Trudy ISA RAN. 2008. T. [1]. S. 166–179 (in Russian).
- Saharov M.K. Novyj adaptivnyj metod mul'timemeticheskoj global'noj optimizacii dlya slabosvyazannyh vychislitel'nyh sistem. Vestnik MGTU im. N.E. Baumana. Ser. Priborostroenie. 2019. № 5. S. 95–114 (in Russian).
- Antamoshin, A.N., Bliznova O.V., Bobov A.V. Intellektual'nye sistemy upravleniya organizacionno-tekhnicheskimi sistemami. M.: GLT. 2016 (in Russian).
- Vasil'ev S.P., Poletaeva N.G. Primenenie metodov mashinnogo obucheniya v zadachah optimizacii. Informacionnye sistemy i tekhnologii: teoriya i praktika: Sb. nauch. tr. SPb. 2019. № 11. S. 28–40 (in Russian).
- Masina O.N. Voprosy upravleniya dvizheniem transportnyh sistem. Transport: nauka, tekhnika, upravlenie. 2006. № 12. S. 10–12 (in Russian).
- Druzhinina O.V., Masina O.N., Petrov A.A. Razrabotka podhoda k resheniyu zadach upravleniya dvizheniem tekhnicheskih sistem, modeliruemyh differencial'nymi vklyucheniyami. Informacionno-izmeritel'nye i upravlyayushchie sistemy. 2017. T. 15. № 4. S. 64–72 (in Russian).
- Nikulin E.A. Osnovy teorii avtomaticheskogo upravleniya. Chastotnye metody analiza i sinteza sistem. SPb.: BHV-Peterburg, 2004 (in Russian).
- Emelyanov S.V., Korovin S.K., Levant A. Sliding modes of higher orders in control systems. Differential Equations. V. 29. № 11. 1993. P. 1877–1899.
- Petrov A.A. Struktura programmnogo kompleksa dlya modelirovaniya tekhnicheskih sistem v usloviyah pereklyucheniya rezhimov raboty. Elektromagnitnye volny i elektronnye sistemy. 2018. T. 23. № 4 S. 61–64 (in Russian).
- Lur'e B.Ya., Enrajt P.Dzh. Klassicheskie metody avtomaticheskogo upravleniya. SPb.: BHV-Peterburg. 2004 (in Russian).
- Openkin D.Yu. Razrabotka i realizaciya algoritma pereklyuchenij na osnove PID-regulyatora po signalu rassoglasovaniya. Mate-rialy Mezhdunarodnogo molodezhnogo nauchnogo foruma «LOMONOSOV-2021». [Elektronnyj resurs] – M.: MAKS Press, 2021 (in Russian).
- Aggarwal C. Neural Networks and Deep Learning. Springer International Publishing. 2019.
- MCST El'brus. Rossijskie mikroprocessory i vychislitel'nye kompleksy [Elektronnyj resurs]. URL= http://www.mcst.ru / (data obrashcheniya: 15.04.2021)