FUZZY NEURAL NETWORK CONTROLLER AS A REAL TIME CONTROLLER USING PSO

Year 2017, Volume: 5 Issue: 1, 15 - 22, 30.01.2017

Sıtkı Öztürk Cihan Karakuzu Melih Kuncan Ahmet Erdil

Abstract

Direct
current (DC) motors are commonly used to control position or speed in many
applications. The speed of the DC motors is adjustable in a wide range with
advantages such as easy control theorems and high performances. DC motors are
used in industrial branches like transportation, electrical train, vehicle,
crane, printer, drivers, paper industry in which adjustable speed and sensitive
position handling are necessarily. In recent years, these applications are
commonly used for household appliance in which low power and low cost are
required with adjustable speed and sensitive position handling as well. In this
study, permanent magnet direct current motor actuator is implemented by using
fuzzy neural network structure. Particle Swarm Optimization
(PSO) algorithm is used as training algorithm of fuzzy neural network
controller. Learning and control in real time is executed in Matlab.
Dynamic performance of the system is observed for constant and variable reference
trajectory of speed.

Keywords

DC Motor Speed Control, ANFIS, Optimization

References

• Jyh-Shing Roger Jang, “ANFIS: Adaptive-Network-Based Fuzzy Inference System”, Transactions on Systems, Man, and Cybernetics, Vol. 23, No: 3, May/June 1993
• http://www.mmo.org.tr/muhendismakina/arsiv/2001/ekim/Genetik_Algoritma.htm (ziyaret tarihi: 01/06/07)
• Karaboga, D., “An Idea Based On Honey Bee Swarm for Numerical Optimization. Technical Report-TR06”, Erciyes University Engineering Faculty Computer Engineering Department, Kayseri, 2005
• Fuqing Zhao, Zongyi Ren, Dongmei Yu, Yahong Yang, “Application of AnImproved Particle Swarm Optimization Algorithm for Neural Network Training”,0-7803-9422-4/05/2005 IEEE
• C. F. Juang, Chao-Hsin Hsu, “Temperature Control by Chip-ImplementedAdaptive Recurrent Fuzzy Controller Designed by Evolutionary Algorithm”, IEEETransactions on Circuits ans Systems-1: Regular Papers, Vol.52, No.11, November, 2005
• C. F. Juang, C. F. Lu,, “Load-frequency control by hybrid evolutionary fuzzyPI controller”, IEE Proc.-Gener. Transm. Distrib, Vol. 153, No: 2, March 2006
• S. P. Ghoshal, “Optimizations of PID gains by particle swarm optimizationsin fuzzy based automatic generation control”, Electric Power Systems Research,Volume 72, Issue 3, s: 203-212, 15 December 2004
• Jih-Gau Juang, Bo-Shian Lin, Kuo-Chih Chin, “Automatic Landing ControlUsing Particle Swarm Optimisation”, Proceedings of the IEEE InternationalConference on Mechatronics, July 10-12 2005, Taipei, Taiwan
• Çura T., “Modern sezgisel teknikler ve uygulamaları, Papatya Yayıncılık Eğitim”, 2008
• Shi, Y. ve Eberhart, R. C. “A modified particle swarm optimizer”, Proceedings of the IEEE International Conference on Evolutionary Computation s: 69-73. IEEE Press, Piscataway, NJ, 1998.
• Jalilvand A., Kimiyaghalam A., Ashouri A., Mahdavi M., “Advanced Particle Swarm Optimization-Based PID Controller Parameters Tuning”, 12th IEEE International Multitopic Conference, Karachi, Pakistan. 2008
• Tamer S, Karakuzu C, “Parçacık Sürüsü Optimizasyon Algoritması ve Benzetim Örnekleri”, ELECO 2006 Elektrik-Elektronik-Bilgisayar Sempozyumu, Elektronik Bildirileri Kitabı, (302-306), Bursa, Türkiye. 2006
• Kennedy, J., Eberhart, R., “Particle Swarm Optimization”, Proceedings of IEEE International Conference on Neural Networks, (pp. 1942-1948), WA, USA. 1995
• Allaoua B., Gasbaoui B., Mebarki B., “Setting Up PID DC Motor Speed Control Alteration Parameters Using Particle Swarm Optimization Strategy”, Leonardo Electronic Journal of Practices and Technologies, ISSN 1583-1078, Issue 14, (p.19-32), 2009