Black Box Model and Evolutionary Fuzzy Control Methods of Coupled-Tank System
In this study, a black box modeling of the coupled-tank system is obtained by using fuzzy sets. The derived model is tested via adaptive neuro fuzzy inference system (ANFIS). In order to achieve a better control performance, the parameters of three different controller types, classical proportional integral controller (PID), fuzzy PID and function tuner method, are tuned by one of the evolutionary computation method, genetic algorithm. All tuned controllers are applied to the fuzzy model of the coupled-tank experimental setup and analyzed under the different reference input values. According to the results, it is seen that function tuner method demonstrates better robust control performance and guarantees the closed loop stability.
[1] L. A. Zadeh, Fuzzy sets, Information and Control, 1965, 338-353.
[2] E. H. Mamdani, Application of fuzzy logic algorithms for control of simple dynamic plant, Proceedings of the Institute of Electrical Engineering, 1974, 121, pp. 1585–1588.
[3] T. Takagi, M. Sugeno, Fuzzy identification of systems and its application to modeling and control, IEEE Trans. Systems, Man and Cybernetics, 1985, 116±132.
[4] H. Ying, “Introduction to Fuzzy Control and Modeling”, Wiley-IEEE Press, 2000.
[5] W. Pedrycz, F. Gomide, “Fuzzy Modeling: Principles and Methodology”, Wiley-IEEE Press, 2007.
[6] J. H. Lilly, “Modeling and Control Methods Useful for Fuzzy Control”, Wiley-IEEE Press, 2010.
[7] D. E. Goldberg, “Genetic Algorithms in Search, Optimization, and Machine Learning”, Boston, MA: Addison-Wesley, 1989.
[8] J. H. Holland, “Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence”, Cambridge, MA: MIT Press, 1992.
[9] O. Cordon, F. Herrera, F. Hoffmann, and L. Magdalena, “Genetic Fuzzy Systems. Evolutionary Tuning and Learning of Fuzzy Knowledge Bases”, Singapore: World Scientific, 2001.
[10] F. Herrera, “Genetic fuzzy systems: Taxonomy, current research trends and prospects,” Evol. Intell., vol. 1, pp. 27–46, 2008.
[11] K.J., Åström, T. Hägglund: “PID controllers: theory, design and tuning”, Instrument Society of America, North Carolina, 1995.
[12] J. L. Hellerstein, Y. Diao, S. Parekh, D. M. Tilbury, “PID Controllers”, Wiley-IEEE Press, 2004.
[13] C.H., Chou, H., Lu, A heuristic self-tuning fuzzy controller, Fuzzy Sets and Systems 61, 249–264, 1994.
[14] C.H., Jung, C.S., Ham, K.I., Lee, A real-time self-tuning fuzzy controller through scaling factor adjustment for the steam generator of NPP, Fuzzy Sets and Systems, 74, 53–60, 1995.
[15] M., Madea, S., Murakami, A self-tuning fuzzy controller., Fuzzy Sets and Systems, 51, 29–40, 1992.
[16] R.K., Mudi, N.R., Pal, A robust self-tuning scheme for PI- and PD-type fuzzy controllers, IEEE Transactions on Fuzzy Systems, 7(1), 2–16, 1999.
[17] W. Z. Qiao, M. Mizumoto, PID type fuzzy controller and parameters adaptive method, Fuzzy Sets and Systems, 1996, 78, 23–35.
[18] Z. W. Woo, H. Y. Chung, J. J. Lin, A PID-type fuzzy controller with self-tuning scaling factors, Fuzzy Sets and Systems, 2000, 115, 321–326.
[19] M. Guzelkaya, I. Eksin, E. Yesil, Self-tuning of PID-type fuzzy logic controller coefficients via relative rate observer, Engineering Applications of Artificial Intelligence, 2003, 227–236.
[20] J. Holland, “Adaptation in natural and artificial systems”, University of Michigan Press, 1975.
[21] J. Shapiro, “Genetic algorithms in machine learning”, Springer, 2001. 6=10
[1] L. A. Zadeh, Fuzzy sets, Information and Control, 1965, 338-353.
[2] E. H. Mamdani, Application of fuzzy logic algorithms for control of simple dynamic plant, Proceedings of the Institute of Electrical Engineering, 1974, 121, pp. 1585–1588.
[3] T. Takagi, M. Sugeno, Fuzzy identification of systems and its application to modeling and control, IEEE Trans. Systems, Man and Cybernetics, 1985, 116±132.
[4] H. Ying, “Introduction to Fuzzy Control and Modeling”, Wiley-IEEE Press, 2000.
[5] W. Pedrycz, F. Gomide, “Fuzzy Modeling: Principles and Methodology”, Wiley-IEEE Press, 2007.
[6] J. H. Lilly, “Modeling and Control Methods Useful for Fuzzy Control”, Wiley-IEEE Press, 2010.
[7] D. E. Goldberg, “Genetic Algorithms in Search, Optimization, and Machine Learning”, Boston, MA: Addison-Wesley, 1989.
[8] J. H. Holland, “Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence”, Cambridge, MA: MIT Press, 1992.
[9] O. Cordon, F. Herrera, F. Hoffmann, and L. Magdalena, “Genetic Fuzzy Systems. Evolutionary Tuning and Learning of Fuzzy Knowledge Bases”, Singapore: World Scientific, 2001.
[10] F. Herrera, “Genetic fuzzy systems: Taxonomy, current research trends and prospects,” Evol. Intell., vol. 1, pp. 27–46, 2008.
[11] K.J., Åström, T. Hägglund: “PID controllers: theory, design and tuning”, Instrument Society of America, North Carolina, 1995.
[12] J. L. Hellerstein, Y. Diao, S. Parekh, D. M. Tilbury, “PID Controllers”, Wiley-IEEE Press, 2004.
[13] C.H., Chou, H., Lu, A heuristic self-tuning fuzzy controller, Fuzzy Sets and Systems 61, 249–264, 1994.
[14] C.H., Jung, C.S., Ham, K.I., Lee, A real-time self-tuning fuzzy controller through scaling factor adjustment for the steam generator of NPP, Fuzzy Sets and Systems, 74, 53–60, 1995.
[15] M., Madea, S., Murakami, A self-tuning fuzzy controller., Fuzzy Sets and Systems, 51, 29–40, 1992.
[16] R.K., Mudi, N.R., Pal, A robust self-tuning scheme for PI- and PD-type fuzzy controllers, IEEE Transactions on Fuzzy Systems, 7(1), 2–16, 1999.
[17] W. Z. Qiao, M. Mizumoto, PID type fuzzy controller and parameters adaptive method, Fuzzy Sets and Systems, 1996, 78, 23–35.
[18] Z. W. Woo, H. Y. Chung, J. J. Lin, A PID-type fuzzy controller with self-tuning scaling factors, Fuzzy Sets and Systems, 2000, 115, 321–326.
[19] M. Guzelkaya, I. Eksin, E. Yesil, Self-tuning of PID-type fuzzy logic controller coefficients via relative rate observer, Engineering Applications of Artificial Intelligence, 2003, 227–236.
[20] J. Holland, “Adaptation in natural and artificial systems”, University of Michigan Press, 1975.
[21] J. Shapiro, “Genetic algorithms in machine learning”, Springer, 2001. 6=10
@article{"International Journal of Electrical, Electronic and Communication Sciences:73325", author = "S. Yaman and S. Rostami", title = "Black Box Model and Evolutionary Fuzzy Control Methods of Coupled-Tank System", abstract = "In this study, a black box modeling of the coupled-tank system is obtained by using fuzzy sets. The derived model is tested via adaptive neuro fuzzy inference system (ANFIS). In order to achieve a better control performance, the parameters of three different controller types, classical proportional integral controller (PID), fuzzy PID and function tuner method, are tuned by one of the evolutionary computation method, genetic algorithm. All tuned controllers are applied to the fuzzy model of the coupled-tank experimental setup and analyzed under the different reference input values. According to the results, it is seen that function tuner method demonstrates better robust control performance and guarantees the closed loop stability.
", keywords = "Function tuner method, fuzzy modeling, fuzzy PID controller, genetic algorithm.", volume = "10", number = "6", pages = "819-6", }
