Контроллер двигателя постоянного тока с нейро-адаптивной нечеткой логикой управления
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Научный журнал Моделирование, оптимизация и информационные технологииThe scientific journal Modeling, Optimization and Information Technology
Online media
issn 2310-6018

DC motor controller with neuro-adaptive fuzzy control logic

Han M.H.   Yakunin A.N.  

UDC 004.67
DOI: 10.26102/2310-6018/2020.29.2.015

  • Abstract
  • List of references
  • About authors

The development of motor drive controllers is an urgent task in industrial and robotic manipulators. DC motor controllers (DCT) can be used in various tasks, as their use provides flexible opportunities for the development of control algorithms. Many well-known controllers that use feedback cannot maintain system performance at an acceptable level. The paper proposes the development of a new DCT controller, which allows to increase the efficiency of its control by reducing tn (rise time), σ (overshoot) and tp (regulation time) when compared with other known controllers (proportional-integral-differential (PID) controller and fuzzy controller (NL) logic). The article discusses the development of a controller based on an adaptive system with neuro-fuzzy logic (ASNL) to effectively control the speed of the DCT with the load. The mathematical model of the developed controller in the environment of Matlab-Simulink is implemented. Comparison of the proposed controller with other known controllers is performed according to the following criteria: tn, tp and σ. To confirm the effectiveness of the proposed ASNL controller, it was compared with known controllers. The simulation results showed that the developed ASNL controller reduces tn compared to the PID controller by 6% and compared to the NL controller by 17%, tp compared to the PID controller by 37% and compared to the NL controller by 17 %, and σ compared to the PID controller by 6% with the load.

1. Moleykutty George. Speed Control of Separately Excited DC Motor. American Journal of Applied Sciences. 2008;5(3):227-233.

2. Yousif Al Mashhadany. Hybrid ANFIS Controller for 6-DOF Manipulator with 3D Model. International Journal of Computers & Technology. 2013;4(2):631-638.

3. H. Usoro, U.T. Itaketo, M. A. Umoren. Control of a dc motor using fuzzy logic algorithm. Nigerian Journal of Technology. 2017;36(2):594-602.

4. Jain, M., Singh, M., Chandra, A., Williamson, S.S. Sensor less Control of Permanent Magnet Synchronous Motor using ANFIS Based MRAS. International Electric Machines& Drives Conference (IEMDC). 2011:599-606.

5. Yaguo. Le, Amira Y. Haikal, Fayz F. Areed. Fault diagnosis of rotating machinery based on multiple ANFIS combination with GAs. Mechanical Systems and Signal Processing. 2007:2280-2294.

6. Basma A. Omar, Zhengjia. He, Yanyang. Zi, Qiao. Hu. An Adaptive Neuro-Fuzzy Speed Controller for a Separately excited DC Motor. International Journal of Computer Applications. 2012;39(9):29-37.

7. Jyh-Shing Roger Jang. Adaptive network based fuzzy inference systems. IEEE Transactions on systems man and cybernetics. 1993;23(3):665-683.

8. Yasser Ali Almatheel, Ahmed Abdelrahman. Speed Control of DC Motor Using Fuzzy Logic Controller. International Conference on Communication, Control, Computing and Electronics Engineering (ICCCCEE). 2017:1-7.

9. N. L. Ismail, K. A. Zakaria, N. S. Moh Nazar, M. Syaripuddin, A. S. N. Mokhtar и S. Thanakodi. Srinivasa Kishore Babub. DC Motor Speed Control using Fuzzy Logic Controller. AIP Conference Proceedings. 2018;1930(1):020026(1-6).

Han Myo Htun

Email: hanmyoe123htun@gmail.com

Institute Microdevices and Control Systems (MDCS)
National Research University "MIET"

Moscow, Russian Federation

Yakunin Alexey Nikolaevich
Dr. Tech. Sci. associate professor
Email: yakunin.alexey@gmail.com

Institute Microdevices and Control Systems (MDCS)
National Research University "MIET"

Moscow, Russian Federation

Keywords: dc motors (dc motors), proportional-integral-differential (pid), fuzzy logic (nl), adaptive neuro-fuzzy output system (asnl)

For citation: Han M.H. Yakunin A.N. DC motor controller with neuro-adaptive fuzzy control logic. Modeling, Optimization and Information Technology. 2020;8(2). Available from: https://moit.vivt.ru/wp-content/uploads/2020/05/HanYakunin_2_20_1.pdf DOI: 10.26102/2310-6018/2020.29.2.015 (In Russ).


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