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The article reviews the task of developing a fuzzy logic PID-type controller for a non-linear dynamic system. The peculiarity of the structure which consists in simplification of its controller by decomposition is presented. The simplest version uses three fuzzy controllers with one input and one output and separate rule bases. The parameters of fuzzy controllers are optimized using a genetic algorithm. A two-step controller setup scheme for a nonlinear dynamic object is proposed. In the first step, the genetic algorithm is used to set up a linear PID-controller, showing that the resulting coefficients are used at the output of each channel of the fuzzy PID-type controller. In the second step, a genetic algorithm forms a nonlinear transformative function for each channel, implemented on the basis of an artificial neural network. The control algorithm is debugged and checked with the MatLab system. The results show a significant improvement in the transition performance compared to traditional controllers.

  • Web Address
  • DOI
  • Date of creation in the UzSCI system 06-08-2020
  • Read count 495
  • Date of publication 20-05-2020
  • Main LanguageIngliz
  • Pages102-108
Ўзбек

Maqolada chiziqli bo‘lmagan dinamik tizimlar uchun PID-turli noqat’iy-mantiqiy rostlagichni ishlab chiqish vazifasi ko‘rib chiqilgan. Rostlagich tuzilishini dekompoziciyalarga ajratish orqali soddalashtirish muhim xususiyatlardan biridir. Eng sodda variantda uchta noqat’iy rostlagich bitta kirish, bitta chiqish va alohida qoida bazalari bilan ishlatiladi. Noqat'iy rostlagichlarning parametrlari genetik algoritm yordamida optimallashtirilgan. Chiziqli bo‘lmagan dinamik obyekt uchun ikki bosqichli rostlagichni sozlash sxemasi taklif etiladi. Birinchi bosqichda chiziqli PID-rostlagichni sozlash uchun genetik algoritm qo‘llaniladi, olingan koeffitsientlar noqat’iy PID-rostlagichning har bir kanalining chiqishida ishlatiladi. Ikkinchi bosqichda, genetik algoritmdan foydalanib sun’iy neyron tarmog‘i asosida amalga oshiriladigan har bir kanal uchun chiziqli bo‘lmagan o‘zgartirish funkciyasi hosil bo‘ladi. Matlab dasturi yordamida boshqarish algoritmi tekshirilgan va sinovdan o‘tkazildi. Olingan natijalar an’anaviy rostlagichlarga qaraganda o‘tish jarayonning xususiyatlari sezilarli yaxshilanishini ko‘rsatadi.

 

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English

The article reviews the task of developing a fuzzy logic PID-type controller for a non-linear dynamic system. The peculiarity of the structure which consists in simplification of its controller by decomposition is presented. The simplest version uses three fuzzy controllers with one input and one output and separate rule bases. The parameters of fuzzy controllers are optimized using a genetic algorithm. A two-step controller setup scheme for a nonlinear dynamic object is proposed. In the first step, the genetic algorithm is used to set up a linear PID-controller, showing that the resulting coefficients are used at the output of each channel of the fuzzy PID-type controller. In the second step, a genetic algorithm forms a nonlinear transformative function for each channel, implemented on the basis of an artificial neural network. The control algorithm is debugged and checked with the MatLab system. The results show a significant improvement in the transition performance compared to traditional controllers.

Русский

В статье рассматриваются результаты решения задачи разработки нечетко-логического регулятора ПИД-типа для нелинейной динамической системы. Представлена особенность структуры, заключающаяся в упрощении ее регулятора путем декомпозиции. В простейшем варианте используются три нечетких регулятора с одним входом и одним выходом и раздельными базами правил. Параметры нечетких регуляторов оптимизируются с использованием генетического алгоритма. Предложена двухшаговая схема настройки регулятора для нелинейного динамического объекта. На первом шаге генетический алгоритм применяется для настройки линейного ПИД-регулятора. Показано, что полученные коэффициенты используются на выходе каждого канала нечеткого регулятора ПИД-типа. На втором шаге с помощью генетического алгоритма формируется нелинейная преобразующая функция для каждого канала, реализуемая на базе искусственной нейронной сети. Алгоритм управления отлажен и проверен с помощью системы MatLab. Полученные результаты показывают значительное улучшение характеристик переходного процесса по сравнению с традиционными регуляторами.

 

 

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№ Author name position Name of organisation
1 Siddikov I.X. Professor TDTU
2 Umurzakova D.M. докторант TDTU
№ Name of reference
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15 15. Siddikov I.X., Iskandarov Z. (2018) Synthesis of adaptive-fuzzy control system of dynamic in conditions of uncertainty of information // International Journal of Advanced Research in Science, Engineering and Technology. Vol. 5. Issue 1. January, P. 5089-5093.
16 16. Siddikov I.X., Umurzakova D.M. (2019) Features of automatic control of technological parameters of water level in the drum steam boilers, Journal of Southwest Jiaotong University. Vol.54. №3. June, 1-10, doi：10.35741/issn.0258-2724.54.3.1.
17 17. Siddikov I.X., Umurzakova D.M. (2019) Mathematical Modeling of Transient Processes of the Automatic Control System of Water Level in the Steam Generator //Universal Journal of Mechanical Engineering, 7(4): 139-146. doi: 10.13189/ujme.2019.070401.
18 18. Sidikov I.X., Umurzakova D.M. (2019) Adaptive neuro-fuzzy regulating system of the temperature mode of the drum boiler // International Journal of Advanced Research in Science, Engineering and Technology. Vol. 6. Issue 1. January. P.7869-7872.
19 19. Soroko E.M. (2006) Golden Sections, Processes of Self-Organizing and Evolution in Systems: Introduction into the General Theory of System Harmonizing. -Moscow: KomKniga, -264 p.
20 20. Umurzakova D.M., Automatic water-level regulating invariant system in the boiler shell // PISC «Advanced Information Technologies and Scientific Computing - 2019». Samara Scientific Center of RAS, -Russia, Samara, 24-26 June 2019 y. P.657-659.
21 21. Siddikov, I.H. and Yadgarova, D.B. (2019) "DEVELOPMENT OF A HIGH-SPEED ALGORITM OF NEURO-LOGICAL CONCLUSION," Technical science and innovation: Vol. 2019: Iss. 1, Article 6. Available at: https://uzjournals.edu.uz/btstu/vol2019/iss1/6.
22 22. Yunusova, S. T. (2019) "SIMULATION OF A TRAINED NEURAL NETWORK OF A FUZZY LOGIC REGULATION SYSTEM BASED ON THE COTTON DRYING PROCESS," Technical science and innovation: Vol. 2019: Iss. 2, Article 7. Available at: https://uzjournals.edu.uz/btstu/vol2019/iss2/7.
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