DC Boost Converter Using a Fuzzy Control System, Simulated in Simulink
( Pp. 30-34)

Decisive for the operation of voltage, current and power converters is the choice of control method. Control of complex dynamic systems in conditions of insufficient or fuzzy information requires the involvement of non-standard approaches during the construction of the control system. There are two possible ways out of the traditional framework of linear control. The first, is to develop more accurate nonlinear models on which high-performance controller design can be based. However, when choosing this way, one has to develop very complex control algorithms, and, consequently, the controller will perform complex mathematical calculations, which can have a negative impact on the control speed and response. Another way is to use a neural network controller based on fuzzy logic. Fuzzy logic is a generalization of mathematical logic and set theory, which is a function of the object belonging to a set and can take any value in the range [0; 1], not only 0 or 1. In the case of fuzzy logic we don't need simulation, because the whole work on designing of the controller is reduced to the transformation of the rules included in the fuzzy logic into the algorithm of automatic control.

Kovalchuk G.N., Khismatullin A.S., (2022), DC BOOST CONVERTER USING A FUZZY CONTROL SYSTEM, SIMULATED IN SIMULINK. Computational Nanotechnology, 4 => 30-34.

Makarova I.M., Lokhina V.M. Intelligent automatic control system. Moscow: Fizmatlit, 2001. 576 с.
Dyakonov V. D93 Simulink 4. A special reference book. St. Petersburg: Piter, 2002. 528 p.: ill.
Altas I.H. Fuzzy logic control in energy systems with design applications in MatLab/Simulink. United Kingdom: The Institution of Engineering and Technology, Michael Faraday House, Six Hills Way, 2017. 506 c.
Mattaveli P., Rossetto L., Spiazzi G. General purpose fuzzy controller for DC-DC converters // IEEE Transactions on Power Electronics. 1997. Vol. 12.
Khismatullin A.S., Gareev I.M. Investigation of integral parameter transfer in liquid with gas bubbles. Ecological Systems and Devices. 2015. No. 7. Pp. 38-42. (In Rus.)
Khismatullin A.S., Prakhov I.V., Grigoriev E.S., Shafeev R.R. Application of fuzzy logic for reactive power compensation in electric network. International Technical and Economic Journal. 2018. No. 4. Pp. 13-19. (In Rus.)
Khismatullin A.S. Method for increasing oil resources transformers with longterm operation. In: IOP conference series: Materials science and engineering. 2018. Pp. 022-058.
Bashirov M.G., Khismatullin A.S., Sirotina E.V. Cooling system oil-immersed transformers with the use of a circulating sulfur hexafluoride // Lecture Notes in Electrical Engineering. 2020. Vol. 641 LNEE. Pp. 613-621.
Vasilev I., Hismatullin A. The theory of fuzzy sets as a means of assessing the periods of service of asynchronous electric motors. In: Proceedings - 2020: International Conference on Industrial Engineering, Applications and Manufacturing, ICIEAM 2020. 2020. Pp. 911-1887.
Prakhov I.V., Khismatullin A.S. Development of a hardware-software complex for determining the technical condition of pumping equipment. In: Proceedings - 2020: International Ural Conference on Electrical Power Engineering, UralCon 2020. 2020. Pp. 220-224.

DC boost converter, modeling, fuzzy control system, controller rules, output voltage graph.