The Results of interaction of the work is development of a method of control that allows simplifying, automating and unifying the process of design of the hybrid systems which are a basis of modern automation. To achieve a definite purpose, a method of control of a technical object based on the construction of an adaptive system of neuro-fuzzy inference is developed. The objects of the system of neuro-fuzzy inference are the classical and neuro-fuzzy models of control. Information exchange between models is provided by means of the developed hybrid control system. The result of the interaction of the two models is the automatic formation of the base of fuzzy controller rules based on knowledge about the control object obtained with its control using the classical controller. In the developed adaptive system of neuro-fuzzy inference signals of error and control in the classical model are used as data for creation a hybrid network. Signals of error and control in a neuro-fuzzy mod el with automatically generated neuro-fuzzy inference rules are used as data to verify the created hybrid network in order to detect the fact of its retraining. Thus, during the control of a technical object by means of a hybrid system, the knowledge of an expert in the subject area for adjusting the parameters of the fuzzy controller are completely eliminated, that allows to control difficultly formalizable objects in conditions of uncertainty. To obtain reliable research results, a hybrid control system, consisting of classical and neuro-fuzzy models is developed. Numerical values of the error and control signals are obtained at discrete time points as a result of the interaction of the two models. Special files for creating and testing a hybrid network in the form of numerical matrixes are generated. The hybrid network is developed in the ANFIS editor of the MATLAB package. The generated structure of the FIS fuzzy inference system of the Sugeno type is graphically shown. The visualization of the dependence of training and verification errors from the number of training cycles is given. The surface of the fuzzy inference system is constructed, that allows estimating the dependence of the output variable on the input.
1. Kolesnikov A.V. Gibridnye intellektual'nye sistemy: teoriya i tekhnologiya razrabotki [Hybrid intelligent systems: theory and technology of development]. St. Petersburg: Izd-vo SPbGTU, 2001, 600 p. 2. Demenkov N.P. Nechetkoe upravlenie v tekhnicheskikh sistemakh: ucheb. posobie [Fuzzy control in technical systems: a training manual]. Moscow: Izd-vo MGTU im. N.E. Baumana, 2005, 200 p. 3. Leonenkov A.V. Nechetkoe modelirovanie v srede MATLAB i fuzzyTECH [Fuzzy modeling in the MATLAB and fuzzyTECH environment]. St. Petersburg: BKhV – Peterburg, 2005, 736 p. 4. Sidikov I.H., Usmanov K.I., Yakubova N.S. Nochiziqli dinamik obyektlarnisinergetik boshqarish usulidan foydalanib sintezlash. Muxammad al-Xorazmiy avlodlari, Ilmiy-amaliy va axborot-tahliliy jurnal. № 1(11) /2020. 5. Thanana Nuchkrua, Thananchai Leephakpreeda. Fuzzy Self-Tuning PID Control of Hydrogen-Driven Pneumatic Artificial Muscle Actuator, Journal of Bionic Engineering, 2013, Vol. 10, pp. 329-340. 6. SHI Dequan, GAO Guili, GAO Zhiwei, XIAO Peng. Application of expert fuzzy pid method for temperature control of heating furnace, Procedia Engineering, 2012, Vol. 29, pp. 257-261. 7. Usmanov K.I, Sarbolayev F.N, Islamova F.K, Yakubova N.S. Adaptivno nechetkoye sinergeticheskoye upravleniye mnogomernix nelineynix dinamicheskix obyektov // Universum: Texnicheskiye nauki: – 2020. – №. 3-1 (72). – S. 24-28. 8. Zhiqiang Yang, Jimin Zhang, Zhongchao Chen, Baoan Zhang. Semi-active control of high-speed trains based on fuzzy PID control, Procedia Engineering, 2011, Vol. 15, pp. 521-525. 9. Sidikov, I., Yakubova, N., Usmanov, K., & Kazakhbayev, S. (2020). Fuzzy synergetic control nonlinear dynamic objects. Karakalpak Scientific Journal, 3(2), 14-22. 10. Usmanov K.I., Babayarov R.A., Avezov T.A., Jabborov A.O. Nechetkoye upravleniye nelineynix dinamicheskix obyektov v intellektualnix sistemax. //Universum: Texnicheskiye nauki: elektron. nauchn. jurn. 2020. № 4(73). 11. Mann G.K.I., Gosine R.G. Three-dimensional min–max-gravity based fuzzy PID inference analysis and tuning, Fuzzy Sets and Systems, 2005, Vol. 156, pp. 300-323. 12. Wu Y., Jiang H., Zou M. The Research on Fuzzy PID Control of the Permanent Magnet Linear Synchronous Motor, Physics Procedia, 2012, Vol. 24, pp. 1311-1318. 13. Sidikov, I., Usmanov, K., Yakubova, N., & Kazakhbayev, S. A. (2020). Nechetkoye sinergeticheskoye upravleniye nelineynix system. Journal of Advances in Engineering Technology, (2). 14. Uteuliev N.U., Yakubova N.S., Usmanov K.I., Yadgarva D.B. System of adaptive control of technological parameters of production of soda //Chemical Technology. Control and Management. – 2018. – T. 2018. – №. 3. – P. 181-185. 15. Abbasi E., Mahjoob M. J., Yazdanpanah R. Controlling of Quadrotor UAV Using a Fuzzy System for Tuning the PID Gains in Hovering Mode, Fourth International Conference on Advances in Computer Engineering – ACE 2013. – Frankfurt, Germany, 2013. Int. j. adv. robot. syst, 2013, Vol. 10, 380:2013. 16. Sidikov, I., Usmanov, K., Yakubova, N. Synergetic fuzzy control of multidimensional nonlinear objects with discrete time // TECHNICAL SCIENCE AND INNOVATION. 2020, №4(06). –P. 134-140. 17. Kai Ou, Ya-Xiong Wang, Zhen-Zhe Li, Yun-De Shen, Dong-Ji Xuan. Feedforward fuzzy-PID control for air flow regulation of PEM fuel cell system, International journal of hydrogen energy, 21 September 2015, Vol. 40, Issue 35, pp. 11686-11695. 18. Siddikov I., Usmanov K., Yakubova N. Synergetic control of nonlinear dynamic objects //Chemical Technology, Control and Management. – 2020. – Т. 2020. – №. 2. – С. 49-55.
"METHOD OF HYBRID CONTROL BASED OF DYNAMIC OBJECTS OF NEURO-FUZZY INFERENCE,"
Karakalpak Scientific Journal: Vol. 5:
2, Article 2.
Available at: https://uzjournals.edu.uz/karsu/vol5/iss2/2