Year 2016,
Volume: 6 Issue: 2, 76 - 84, 05.12.2016
Sami Şit
,
Erdal Kılıç
,
Hasan Rıza Özçalık
Hussein Alruim Alhasan
Ahmet Gani
References
- Bhattacharya, S., Chatterjee, A., Munshi, S., (2003). An Improved PID-type Fuzzy Controller Employing Individual Fuzzy P, Fuzzy I and Fuzzy D Controllers. Transactions of the Institute of Measurement and Control 25, 352–372 pp.
- Gani, A., Acikgoz, H., Kilic, E., Ozcalik, H.R., (2014a). Dynamic Performance Analysis of Inverted Pendulum System by using PD-Fuzzy Logic Controller and PD Controller. International Refereed Journal of Engineering And Sciences, Vol:1(2), 1-13 pp.
- Gani, A., Acikgoz, H., Kilic, E., Sit, S., (2014b). Fuzzy Logic Based Control of Inverted Pendulum. ELECO2014, 27-29 November 2014. Bursa, Turkey.
- Gani, A., Ozcalik, H.R., Sekkeli, M., Kececioglu, O.F., Sit, S., (2016). Dynamic Performance Comparison of PD and PD-Fuzzy Logic Controllers for Inverted Pendulum System. International Conference on Natural Science and Engineering (ICNASE’16), Kilis, Turkey.
- Goher, K. M., Tokhi, M. O., (2010). A New Configuration of Two-Wheeled Inverted Pendulum: A Lagrangian-Based Mathematical Approach, Multidisciplinary Journals in Science and Technology, Journal of Selected Areas in Robotics and Control (JSRC), December Edition.
- Hasan, M. Saha, C., Rahman, M. M., Sarker, M.R.I., Aditya, S. K., (2012). Balancing of an Inverted Pendulum Using PD Controller, Dhaka Univ. J. Sci. 60(1), 115-120 pp.
- Hellendoom, H., Thomas, C., (1993). Defuzzification in Fuzzy Controllers, Intelligent and Fuzzy Systems, vol. 1, 109-123 pp.
- Juang, C. F. and Chang, Y. C., (2011). Evolutionary-Group-Based Particle-Swarm-Optimized Fuzzy Controller with Application to Mobile-Robot Navigation in Unknown Environments, in IEEE Transactions on Fuzzy Systems, 19(2), 379-392 pp.
- Khosla, A., Leena, G., Soni, M. K., (2014). Various Control Techniques and their Performance Analysis for Inverted Pendulum, Int. J. on Recent Trends in Engineering and Technology, 11 (1).
- Kilic, E., Ozcalik, H.R., Yilmaz, S., Sit, S., (2015). A Comparative Analysis of FLC and ANFIS Controller for Vector Controlled Induction Motor Drive, 2015 IEEE International Aegean Conference on Electrical Machines & Power Electronics (ACEMP2015), 2-4 September 2015, Side-Antalya, Turkey.
- Lee, C.C., (1990). Fuzzy Logic in Control Systems: Fuzzy Logic Controller-Part I, IEEE Transactions on System Man and Cybernetics. Vol. SMC-20, no. 2, Marc/ April, 404-418 pp.
- Osim, A.A.A., Aoki, A.R., (2002). Particle Swarm Optimization for Fuzzy Membership Functions Optimization, Proc. 2002 IEEE International Conference on Systems, Man and Cybernetics, vol. 3, Oct. 6-10 pp.
- Ozcalik, H. R., (2006), An efficient DC Servo Motor Control Based on Neural Noncausal Inverse Modeling of The Plant, ISNN'06 Proceedings of the Third International Conference on Advances in Neural Networks, 1075-1083 pp.
- Ozcalik, H.R., Gani, A., Acikgoz, H., Kececioglu, O.F., (2014a). The Performance Analysis of Permanent Magnet DC Motor Speed Control with PI-Fuzzy Logic Control Method. ISITES 2014 2nd International Symposium on Innovative Technologies In Engineering And Science, Karabuk University Congress and Culture Center.
- Ozcalik, H.R., Gani, A., Kilic, E., Kececioglu, O.F., (2014b). DC Servo Motor Speed Control in Nonlinear Load by Using Different Fuzzy Membership Functions. Academic Platform Journal of Engineering and Science, 2(3), 1-8 pp.
- Sevil, M., Elalmış, N., Görgün, H., Aydın, N., (2015). Control of Air Conditioning with Fuzzy Logic Controller Design for Smart Home Systems, Sigma Journal of Engineering and Natural Sciences, 33(3), 439-463 pp.
- Sugeno, M., (1985). Industrial Applications of Fuzzy Control, Elsevier Science Inc.
- Takagi, T., Sugeno, M., (1985). Fuzzy Identification of Systems and Its Applications to Modeling and Control. IEEE Trans. Syst. Man Cybern., 15, 116–132 pp.
- Wang, H.O., Tanaka, K., Griffin, M., (1995). Parallel Distributed Compensation of Non-linear Systems by Takagi–Sugeno Fuzzy Model, Proc. 4th IEEE Int. Conf. Fuzzy Syst., Yokohama, 531–538 pp.
- Yadav, A. K., Gaur, P., Mittal, A. P., and Anzar M., (2011). Comparative Analysis of Various Control Techniques for Inverted Pendulum, in Proc. India International Conference on Power Electronics, 1-6 pp.
- Yaras, B., Hüseynov, R., Namazov, M., Çelikkale, I.E., Şeker, M., (2014) Fuzzy Control and Sliding Mode Fuzzy Control of DC Motor, Sigma Journal of Engineering and Natural Sciences 32, 97-108 pp.
Modelling and Performance Comparison of PD and Takagi-Sugeno Type Fuzzy Logic Controllers for Inverted Pendulum System
Year 2016,
Volume: 6 Issue: 2, 76 - 84, 05.12.2016
Sami Şit
,
Erdal Kılıç
,
Hasan Rıza Özçalık
Hussein Alruim Alhasan
Ahmet Gani
Abstract
In recent years, fuzzy logic based modeling and control methods have been increasingly used in both industrial applications and academic field. Inverted pendulum system consists of conventional materials with nonlinear dynamic structure to test types of controllers. This paper presents a simulation study of Takagi-Sugeno type Fuzzy Logic Controllers for an inverted pendulum (cart-pole) system. A Takagi-Sugeno method was applied to the inverted-pendulum system, which can balance the pendulum over a bigger range of pole angle and cart position. Fuzzy logic controller proposed in this study is compared with the conventional PD controller in order to demonstrate the successes of the proposed controller. This study aims to determine the best control strategy that provides better performance with reference to pendulum's angle and cart's position. A Takagi-Sugeno type Fuzzy Logic Controller yields better results in terms of rise time, settling time, overshoot and steady state error compared to PD controller. Simulation results are shown to prove the effectiveness and robustness of the suggested fuzzy controller.
References
- Bhattacharya, S., Chatterjee, A., Munshi, S., (2003). An Improved PID-type Fuzzy Controller Employing Individual Fuzzy P, Fuzzy I and Fuzzy D Controllers. Transactions of the Institute of Measurement and Control 25, 352–372 pp.
- Gani, A., Acikgoz, H., Kilic, E., Ozcalik, H.R., (2014a). Dynamic Performance Analysis of Inverted Pendulum System by using PD-Fuzzy Logic Controller and PD Controller. International Refereed Journal of Engineering And Sciences, Vol:1(2), 1-13 pp.
- Gani, A., Acikgoz, H., Kilic, E., Sit, S., (2014b). Fuzzy Logic Based Control of Inverted Pendulum. ELECO2014, 27-29 November 2014. Bursa, Turkey.
- Gani, A., Ozcalik, H.R., Sekkeli, M., Kececioglu, O.F., Sit, S., (2016). Dynamic Performance Comparison of PD and PD-Fuzzy Logic Controllers for Inverted Pendulum System. International Conference on Natural Science and Engineering (ICNASE’16), Kilis, Turkey.
- Goher, K. M., Tokhi, M. O., (2010). A New Configuration of Two-Wheeled Inverted Pendulum: A Lagrangian-Based Mathematical Approach, Multidisciplinary Journals in Science and Technology, Journal of Selected Areas in Robotics and Control (JSRC), December Edition.
- Hasan, M. Saha, C., Rahman, M. M., Sarker, M.R.I., Aditya, S. K., (2012). Balancing of an Inverted Pendulum Using PD Controller, Dhaka Univ. J. Sci. 60(1), 115-120 pp.
- Hellendoom, H., Thomas, C., (1993). Defuzzification in Fuzzy Controllers, Intelligent and Fuzzy Systems, vol. 1, 109-123 pp.
- Juang, C. F. and Chang, Y. C., (2011). Evolutionary-Group-Based Particle-Swarm-Optimized Fuzzy Controller with Application to Mobile-Robot Navigation in Unknown Environments, in IEEE Transactions on Fuzzy Systems, 19(2), 379-392 pp.
- Khosla, A., Leena, G., Soni, M. K., (2014). Various Control Techniques and their Performance Analysis for Inverted Pendulum, Int. J. on Recent Trends in Engineering and Technology, 11 (1).
- Kilic, E., Ozcalik, H.R., Yilmaz, S., Sit, S., (2015). A Comparative Analysis of FLC and ANFIS Controller for Vector Controlled Induction Motor Drive, 2015 IEEE International Aegean Conference on Electrical Machines & Power Electronics (ACEMP2015), 2-4 September 2015, Side-Antalya, Turkey.
- Lee, C.C., (1990). Fuzzy Logic in Control Systems: Fuzzy Logic Controller-Part I, IEEE Transactions on System Man and Cybernetics. Vol. SMC-20, no. 2, Marc/ April, 404-418 pp.
- Osim, A.A.A., Aoki, A.R., (2002). Particle Swarm Optimization for Fuzzy Membership Functions Optimization, Proc. 2002 IEEE International Conference on Systems, Man and Cybernetics, vol. 3, Oct. 6-10 pp.
- Ozcalik, H. R., (2006), An efficient DC Servo Motor Control Based on Neural Noncausal Inverse Modeling of The Plant, ISNN'06 Proceedings of the Third International Conference on Advances in Neural Networks, 1075-1083 pp.
- Ozcalik, H.R., Gani, A., Acikgoz, H., Kececioglu, O.F., (2014a). The Performance Analysis of Permanent Magnet DC Motor Speed Control with PI-Fuzzy Logic Control Method. ISITES 2014 2nd International Symposium on Innovative Technologies In Engineering And Science, Karabuk University Congress and Culture Center.
- Ozcalik, H.R., Gani, A., Kilic, E., Kececioglu, O.F., (2014b). DC Servo Motor Speed Control in Nonlinear Load by Using Different Fuzzy Membership Functions. Academic Platform Journal of Engineering and Science, 2(3), 1-8 pp.
- Sevil, M., Elalmış, N., Görgün, H., Aydın, N., (2015). Control of Air Conditioning with Fuzzy Logic Controller Design for Smart Home Systems, Sigma Journal of Engineering and Natural Sciences, 33(3), 439-463 pp.
- Sugeno, M., (1985). Industrial Applications of Fuzzy Control, Elsevier Science Inc.
- Takagi, T., Sugeno, M., (1985). Fuzzy Identification of Systems and Its Applications to Modeling and Control. IEEE Trans. Syst. Man Cybern., 15, 116–132 pp.
- Wang, H.O., Tanaka, K., Griffin, M., (1995). Parallel Distributed Compensation of Non-linear Systems by Takagi–Sugeno Fuzzy Model, Proc. 4th IEEE Int. Conf. Fuzzy Syst., Yokohama, 531–538 pp.
- Yadav, A. K., Gaur, P., Mittal, A. P., and Anzar M., (2011). Comparative Analysis of Various Control Techniques for Inverted Pendulum, in Proc. India International Conference on Power Electronics, 1-6 pp.
- Yaras, B., Hüseynov, R., Namazov, M., Çelikkale, I.E., Şeker, M., (2014) Fuzzy Control and Sliding Mode Fuzzy Control of DC Motor, Sigma Journal of Engineering and Natural Sciences 32, 97-108 pp.