SYNTHESIS OF SYNERGETIC FUZZY CONTROLLERS FOR LINEAR PERFORMANCE SYSTEMS
Abstract
The paper proposes a method for synthesizing an adapƟve fuzzy synergisƟc controller with discrete Ɵme
for indefinite nonlinear dynamic objects, with changing parameters in Ɵme. The synthesized adapƟve controller takes into
account the non-linear nature of the object, allows its parameters to adapt to environmental changes: The synthesis of the
controller is carried out by the hybrid applicaƟon of the methods of the theory of synergisƟc control and fuzzy system and
gives the system asymptoƟc stability and the desired dynamic properƟes of the control system, with the ability to adapt to
changes in the parameters of the object. The proposed method of synergisƟc control guarantees the reliability and
asymptoƟc stability of the control system and makes it possible to use typical and atypical control laws. To overcome the
difficulƟes associated with the uncertainƟes of the funcƟon of the states of objects, the use of a neural network model of
the Mamdani type is proposed. A sigmoidal funcƟon is used as the membership funcƟon, which is disƟnguished by the
simplicity of implementaƟon and the possibility of differenƟaƟng input variables. The resulƟng control law has an
analyƟcal dependence, which significantly increases the possibiliƟes of its implementaƟon on microcontrollers. The
simulaƟon results by example showed the effecƟveness of the proposed method relaƟve to the known ones
Keywords
Adaptation, discreteness, nonlinearity, synergistic control, synthesis, regulator, analytical dependence, macrovariables, invariant, stability, uncertainty, control quality.
References
- 1. Dadras S., Momeni H.R. Control uncertain Genesio – Tesi chaotic system: Adaptive sliding mode approach // Chaos, Solitons
- and Fractals. – 2009. – Vol. 42. – Pp. 3140 – 3146
- 2. Fradkov A.L., Evans R.J. Control of chaos: methods and application in engineering // Ann. Rev. Control. – 2005. – Vol. 29. – Pp.
- 33 – 56.
- 3. Ho N.F., Wong Y.K., Rad A.B. Adaptive fuzzy sliding mode control with chattering elimination for nonlinear SISO systems.
- Simulation Modeling Practice and Theory. Vol. 17, no. 7, 2009. -pp. 1119-1120.
- 4. Kolesnikov A. A. Synergistic control theory. - M.: Energoatomizdat, 1994.
- 5. Kolesnikov A.A. Synergistic methods of managing complex systems: the theory of system synthesis. -M.: Com Book, 2006.
- 6. Khidirova Ch.M. Comparative Analysis of Artificial Neural Network Training Algorithms. International Conference
- on Information Science and Communications Technologies (ICISCT 2020). Tashkent, Uzbekistan, 2020. DOI:10.1109/
- ICISCT50599.2020.9351395.
- 7. Khidirova Ch.M. Methods and algorithms of determination of complexity of test questions for formation a database system of
- the adaptive test-control of knowledge. International Conference on Information Science and Communications Technologies (ICISCT
- 2017). Tashkent, Uzbekistan, 2017. -P. 2-5. DOI:10.1109/ICISCT.2017.8188572.
- 8. Kolesnikov A.A., Kolesnikov A.A., Kuzmenko A.A. The ACAR method and the theory of adaptive control in the problems of
- synthesis of nonlinear control systems // Mechatronika, avtomatizatsiya, upravlenie. - 2017. - T.18. - No. 9. (in the press).
- 9. Marakhimov A.R., Siddiqov I.H., Nasriddinov A., Byun J. –Y. 2015, Lecture Notes in Electrical Engineering 330.
- 10. Nigmatova F.U., Shomansurova M.Sh., Siddikov I.Kh., Musakhonov A.A. 2014. Automation and Remote Eontrd 756.
- 11. Sarbolaev F.N., Islamova F.K., Yakubova N.S., Usmanov K.I. Adaptively Fuzzy Synergistic Control of Multidimensional
- Nonlinear Dynamic Objects, Universum: Engineering Sciences. №3(72), 2020. -24 p.
- 12. Synergistic methods of managing complex systems: energy systems / Edited by A.A. Kolesnikov. – M.: Editorial URSS, 2005.
- 13. Young D.S., Won H., Santi E., Monti A. Synergistic control approach for induction motor speed control. 30th Annual
- Conference of the IEEE Industrial Electronics Society, November 2 - 6, 2004, Busan, Korea.
- 14. Siddikov Isamidin Xakimovich, Bakhrieva Xurshida Askarxodjaevna Designs Neuro-Fuzzy Models in Control Problems of a
- Steam Heater // Universal Journal of Electrical and Electronic Engineering 6(5), 2019.-P. 359-365. (№29; Scopus; IF:0.283).
- 15. Siddikov Isamiddin Xakimovich, Umurzakova Dilnoza Maxamadjonovna and Bakhrieva Hurshida Askarxodjaevna
- Adaptive system offuzzy-logical regulation by temperature mode of a drum boiler // IIUM Engineering Journal, Vol.21, №.l, 2020.-P.
- 182-192. (№6; Scopus; IF:0.281).
- 16. Sidikov, Kh. Bakhrieva. Synthesis of the synergetic law of control of nonlinear dynamic objects // Technical science and
- innovation Journal, №1/2023, P.111-121.