525

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.

  • Ссылка в интернете
  • DOI
  • Дата создание в систему UzSCI 06-08-2020
  • Количество прочтений 491
  • Дата публикации 20-05-2020
  • Язык статьиIngliz
  • Страницы102-108
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.

 

Ключевые слова
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.

 

 

Ключевые слова
№ Имя автора Должность Наименование организации
1 Siddikov I.X. Professor TDTU
2 Umurzakova D.M. докторант TDTU
№ Название ссылки
1 1. Aidan O’Dwyer. (2009) Handbook of PI and PID Controller Tuning Rules. 3rd Edition. Dublin: Institute of Technology; Ireland, Imperial College Press. -529 p.
2 2. Astrom K.J. (2004) Revisiting the Ziegler-Nichols step response method for PID control/ K. J. Astrom, T. Hagglund // Journal of Process Control. №4 P.635-650.
3 3. Astrom K.J. (2006) Advanced PID control/ K. J. Astrom, T. Hagglund – ISA (The instrumentation, Systems, and Automation Society), – 460 p.
4 4. Astrom K.J. (2006) Advanced PID control/ K. J. Astrom, T. Hagglund – ISA (The instrumentation, Systems, and Automation Society), 2006 . – 460 p.
5 5. Demchenko V.A. (2001) Automation and modeling of technological processes at nuclear power plants and thermal power plants / V. A. Demchenko. - Odessa: Astroprint, - 308 p.
6 6. Kim T., Maruta I., Sugie T. (2008) Robust PID controller tuning based on the constrained particle swarm optimization //Automatica. Vol. 44. № 4, P. 1104–1110.
7 7. Kulakov G.Т., Kravchenko V.V., Makosko Yu.V. (2012, 2013) Economical Efficiency Evaluation Technique of Implementing Innovation Systems in the TPP Automatic Control Systems. Part 1. Nauka i Tekhnika [Science & Technique], 5, 92–97, 2, 77–82 (in Russian).
8 8. Leigh J.R. (2004) Neural networks, fuzzy logic, genetic algorithms, learning systems intelligent systems, Control Theory, doi: 10.1049/PBCE064E_ch.
9 9. Lipatnikov G.A., Guzeev M.S. (2007) Avtomaticheskoe regulirovanie ob’ektov teploenergetiki [Automatic control of heat power facilities]. Vladivostok: Dal'nevostochnyi gosudarstvennyi tekhnicheskii universitet ¬- 225 p.
10 10. Lu C., Hsu C., Juang C. (2013) Coordinated control of flexible AC transmission system devices using an evolutionary fuzzy lead-lag controller with advanced continuous ant colony optimization //IEEE Transactions on Power Systems. Vol. 28. № 1, P. 385–392.
11 11. Pelusi D. (2012) PID and intelligent controllers for optimal timing performances of industrial actuators //International Journal of Simulation: Systems, Science and Technology, Vol. 13, № . 2, Р. 65–71.
12 12. Pelusi D., Mascella R. (2013) Optimal control algorithms for second order systems //Journal of Computer Science. Vol. 9. №2. P. 183–197.
13 13. Rotach V.Ya. (2008) The Theory of Automatic Control. -Moscow: Publishing House Moscow Power Engineering Institute, - 396 p.
14 14. Rutkovskaya D., Pilinskiy M., Rutkovskiy L. Neyronnie seti, geneticheskie algoritmi i nechetkie sistemi- M.: Goryachaya liniya-Telekom, 2006. P. 452.
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.
В ожидании