Abstract
The main purpose of this research is to investigate stable synthesis algorithms of a neural network regulator for controlling dynamic systems. In this research, the synthesis of neural network regulator and analysis of the modern condition of intelligent control systems has been conducted. This paper presents stable synthesis algorithms of a multi-mode neural network controller on the basis of methods for solving variational inequalities, which can ensure the consistency of desired estimations and high accuracy of the intelligent control system. Based on estimation theory, the regular algorithms have been proposed in combination with adaptive identification algorithms. These derived algorithms can provide consistency of desired estimations with convergence properties and can be used in solving different kinds of problems related to system synthesis for controlling dynamic objects used for several functional purposes.
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References
Sigeru, O., Khalid, M.B., Rubiyah, Y.: Neuro-Control and its Applications. Springer, London (1996)
Rutkovskaya, D., Pilinsky, M., Rutkovsky, L.: Neural Networks, Genetic Algorithms and Fuzzy Systems. Hotline-Telecom, Moscow (2004)
Terekhov, V.A., et al.: Neural Network Control Systems. Book 8. (Neurocomputers and Their Application). IPRZHR, Moscow (2002)
Vasiliev, V.I., Panteleev, S.V.: Neurocontrol - a new section of the theory of complex systems control. Neurocomput. Dev. Appl. 5, 33–45 (2005)
Uskov, A.A., Kuzmin, A.V.: Intelligent Control Technologies. Artificial Neural Networks and Fuzzy Logic. Hotline-Telecom, Moscow (2004)
Kocijan, J.: Modelling and Control of Dynamic Systems Using Gaussian Process Models. Springer, New York (2016)
Yusupbekov, N.R., Marahimov, A.R., Gulyamov, Sh.M., Igamberdiev, H.Z.: APC fuzzy model of estimation of cost of switches at designing and modernizations of data-computing networks. In: AICT 2010, pp. 244–249 (2010). https://doi.org/10.1109/icaict.2010.5612015
Xakimovich, S.I., Maxamadjonovna, U.D., Askarxodjaevna, B.H.: Adaptive system of fuzzy-logical regulation by temperature mode of a drum boiler. IIUM Engin. J. 21(1), 182–192 (2020). https://doi.org/10.31436/iiumej.v21i1.1220
Vasiliev, V.I., Ilyasov, B.G.: Intelligent Control Systems. Theory and Practice. Radio Engineering, Moscow (2009)
Egupov, N.D.: Methods of Robust, Neuro-Fuzzy and Adaptive Control. Bauman Mosc. State Tech. Univ., Moscow (2002)
Verlan, A.F., Sizikov, V.S.: Integral Equations: Methods, Algorithms, Programs. Naukovadumka, Kiev (1986)
Kolomeitseva, M.B., Ho, D.L.: Adaptive control systems for dynamic objects based on fuzzy controllers. Sputnik+Company, Moscow (2002)
Yusupbekov, N.R., Igamberdiev, H.Z., Sevinov, J.U.: Formalization of identification procedures of control objects as a process in the closed dynamic system and synthesis of adaptive regulators. J. Advan. Res. Dyn. Cont. Sys. 12(06), 77–88 (2020). https://doi.org/10.5373/JARDCS/V12SP6/SP20201009
Yusupbekov, N.R., Igamberdiev, H.Z., Mamirov. U.F.: Algorithms of sustainable estimation of unknown input signals in control systems. J. Mult. Val. Log. Soft Comput. 33(1–2), 1–10 (2019). https://www.oldcitypublishing.com/pdf/9291
Sevinov, J.U., Zaripov, O.O., Zaripova, Sh.O.: The algorithm of adaptive estimation in the synthesis of the dynamic objects control systems. Inter. J. Adv. Sci. Tech., 29(5), 1096–1100 (2020). http://sersc.org/journals/index.php/IJAST/article/view/7887
Alifanov, O.M., Artyukhin, E.A., Rumyantsev, S.V.: Extremal Methods for Solving Ill-Posed Problems. Science, Moscow (1988)
Bakushinsky, A.B., Goncharsky, A.V.: Iterative Solution Methods for Ill-Posed Problems. Science, Moscow, (1989). (in Russian)
Vainikko, G.M., Veretennikov, A.Yu.: Iterative Procedures in Ill-Posed Problems. Science, Moscow (1986)
Igamberdiev, H.Z., Sevinov, J.U., Yusupbekov, A.N.: Regular algorithms for identifying the parameters of an object and a controller in a closed-loop control system. J. Chem. Tech. Cont. Manag. 6, 50–54 (2017)
Zaripov, O.O., Shukurova, O.P., Sevinov, J.U.: Algorithms for identification of linear dynamic control objects based on the pseudo-concept concept. Inter. J. Psy. Rehab. 24(3), 261–267 (2020). https://doi.org/10.37200/IJPR/V24I3/PR200778
Bakushinsky, A.B., Kokurin, M.Yu.: Iterative methods for solving irregular equations. Lenand, Moscow (2006)
Igamberdiyev, H.Z., Yusupbekov, A.N., Zaripov, O.O., Sevinov, J.U.: Algorithms of adaptive identification of uncertain operated objects in dynamical models. Procedia Comput. Sci. 120, 854–861 (2017). https://doi.org/10.1016/j.procs.2017.11.318
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Yusupbekov, A.N., Sevinov, J.U., Mamirov, U.F., Botirov, T.V. (2021). Synthesis Algorithms for Neural Network Regulator of Dynamic System Control. In: Aliev, R.A., Kacprzyk, J., Pedrycz, W., Jamshidi, M., Babanli, M., Sadikoglu, F.M. (eds) 14th International Conference on Theory and Application of Fuzzy Systems and Soft Computing – ICAFS-2020 . ICAFS 2020. Advances in Intelligent Systems and Computing, vol 1306. Springer, Cham. https://doi.org/10.1007/978-3-030-64058-3_90
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