IMITATION OF FUZZY LOGIC CONTROLLER BASED ARTIFICIAL NEURAL NETWORK, AND APPLICATION OF INVERTED PENDULUM SYSTEM CONTROL

Year 2019, Volume: 9 Issue: 2, 121 - 136, 30.12.2019

Mehmet Serhat Can Murat Sam

https://doi.org/10.36222/ejt.650617

Abstract

Fuzzy Logic Controllers (FLCs) are
effective solutions for nonlinear and parameter variability systems, but it
contains multiple mathematical operations causing the controller to react
slowly. This study aims to obtain a controller that can imitate the effective
control performance of the FLC, which is easy to design both in software and
hardware, and has a short response time. Artificial neural networks (ANNs)
provide effective solutions in system modeling. Modeling of FLC has been
realized by using of ANN’s learning and parallel processing capability. The
design process of the FLC and the training processes of the ANN were studied in
Matlab SIMULINK environment. In the study, FLC was modelled at high similarity
ratio with small ANN structure. ANN results were obtained very faster than the
FLC control performance. The control performances of two controllers were
observed to be very close to each other. As a result, ANN model has smaller
structure than FLC, which makes it possible to implement the controller easily
in terms of hardware and software.

Keywords

Fuzzy logic controller, artificial neural network

References

• [1] Sayğan, S. (2014). Örgüt Biliminde Karmaşıklık Teorisi. Ege Academic Review, 14(3).
• [2] Ramezani, M. R., Kamyad, A. V. (2010). Approximation of general nonlinear control systems with linear time varying systems. In 2010 18th Iranian Conference on Electrical Engineering (pp. 680–685). Presented at the 2010 18th Iranian Conference on Electrical Engineering, Isfahan, Iran. https://doi.org/10.1109/IRANIANCEE.2010.5506987
• [3] Altaş, İ. H. (1999). Bulanık Mantık: Bulanıklılık Kavramı. Enerji, Elektrik, Elektromekanik-3e, 62, 80–85.
• [4] Pamuk, Z., Yurtay, Y., Yavuzyilmaz, O. (2015). Establishing the Potential Clients Using Artificial Neural Networks. Balkan Journal of Electrical and Computer Engineering, 3, 219–224.
• [5] Yegnanarayana, B. (2009). Artificial Neural Networks. PHI Learning Pvt. Ltd.
• [6] Tolon, M., Tosunoğlu, N. G. (2008). Tüketici Tatmini Verilerinin Analizi: Yapay Sinir Ağlari Ve Regresyon Analizi Karşilaştirmasi. Gazi Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 10(2), 247–259.
• [7] Ergezer, H., Dikmen, M., Özdemir, E. (2003). Yapay sinir ağları ve tanıma sistemleri. PiVOLKA, 2(6), 14–17.
• [8] Kilic, E., Ozbalci, U., Ozcalik, H. R. (2012). Lineer Olmayan Dinamik Sistemlerin Yapay Sinir Ağları ile Modellenmesinde MLP ve RBF Yapılarının Karşılaştırılması. ELECO2012 Elektrik-Elektronik ve Bilgisayar Mühendisliği Sempozyomu,(29.11. 2012-01.12. 2012).
• [9] Tasdemir, S. (2018). Artificial Neural Network Model for Prediction of Tool Tip Temperature and Analysis. International Journal of Intelligent Systems and Applications in Engineering, 6(1), 92–96. https://doi.org/10.18201/ijisae.2018637937
• [10] Diaz, N. L., Soriano, J. J. (2007). Study of Two Control Strategies Based in Fuzzy Logic and Artificial Neural Network Compared with an Optimal Control Strategy Applied to a Buck Converter. In NAFIPS 2007 - 2007 Annual Meeting of the North American Fuzzy Information Processing Society (pp. 313–318). Presented at the NAFIPS 2007 - 2007 Annual Meeting of the North American Fuzzy Information Processing Society. https://doi.org/10.1109/NAFIPS.2007.383857
• [11] Efe, M. Ö. (2011). Neural Network-Assisted PIλDμ Control. Fractional Dynamics and Control pp 19-31doi: 10.1007/978-1-4614-0457-6_2
• [12] Efe, M. Ö. (2011). Neural Network Assisted Computationally Simple PIλDμ Control of a Quadrotor UAV. IEEE Transactions On Industrial Informatics, vol. 7, no. 2
• [13] Efe, M. Ö., Kaynak, O., Abadoglu, E. (1999). Neural Network Assisted Nonlinear Controller For A Bioreactor, International Journal of Robust and Nonlinear Control 9(11),799-815doi: 10.1002/(SICI)1099-1239(199909)9:11<799::AID-RNC441>3.0.CO;2-U
• [14] Marini, F., Bucci, R., Magrì, A. L., Magrì, A. D. (2008). Artificial neural networks in chemometrics: History, examples and perspectives. Microchemical Journal, 88(2), 178–185. https://doi.org/10.1016/j.microc.2007.11.008
• [15] Sreelakshmi, K., Ramakanthkumar, P. (2008). Neural networks for short term wind speed prediction. World Academy of Science, Engineering and Technology, 42, 721–725.
• [16] Haykin, S. S. (2009). Neural networks and learning machines/Simon Haykin. New York: Prentice Hall,.