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In this article, we first chose the design settings of the fuzzy PID controllers (FPIDC) so that the FPIDCs mimic the classical PID controllers. The advantage of these controllers is the combination of the simplicity of the classical PID controllers and the interpretability of fuzzy controllers which makes the task of parameters tuning easier. Secondly, we present a method for optimizing the closed-loop system consisting of a FPIDC and an unknown plant using the Q-learning algorithm (QLA). Specifically, QLA minimizes a cost function which quantifies the performance of FPIDC. Without loss of generality the square error sum cost function is used. The QLA, which is a nonmodel-based method, iteratively search of the best parameters so that the output of the cost function is less then satisfaction threshold. Finally, a simulation example is used to prove the effectiveness of the proposed method.
Czasopismo
Rocznik
Tom
Strony
415--435
Opis fizyczny
Bibliogr. 21 poz.
Twórcy
autor
autor
autor
autor
- LAMEL, Faculte des Sciences de l'Ingnieur, University of Jijel, Jijel, Algeria, boubert_hamid@yahoo.com
Bibliografia
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- [3] H. BOUBERTAKH and P. Y. GLORENNEC: Optimization of a fuzzy PI controller using reinforcement learning. Proc. IEEE Int. Conf on Information and Communication Technologies: From Theory to Applications, Damascus, (2006), 1657- 1662.
- [4] H.-Y. CHUNG, B.-C. CHEN and J.-J. LIN: A PI-type fuzzy controller with selftuning scaling factors. Fuzzy Sets and Systems, 93 (1998), 23-28.
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- [6] H.-B. DUAN, D.-B WANG and X.-F. Yu: Novel approach to nonlinear PID parameter optimization using Ant Colony optimization algorithm. J. of Bionic Engineering, 3 (2006), 73-78.
- [7] J. JANTZEN: Foundations of fuzzy control. West Sussex (England), John Wiley &Sons Ltd, 2007.
- [8] C.-N. Ko, T.-L. LEE, H.-T. FAN and C.-J. Wu: Genetic auto-tuning and rule reduction of fuzzy PID controllers. Proc. IEEE Mt. Conf on Systems. Man, and Cybernetics, Taipei, Taiwan, (2006), 1096-1101.
- [9] B. M. MOHAN and AMBALAL V. PATEL: Analytical structures and analysis of the simplest fuzzy PD controllers. IEEE Trans. Systems, Man, and Cybernetics, Part B: Cybernetics, 32 (2002), 239-248.
- [10] B. M. MOHAN and A. SINHA: Analytical structure and stability analysis of a fuzzy PID controller. Applied Soft Computing, 8 (2008), 749-758.
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- [12] M. MUZIMUTO: Realization of PID controls by fuzzy control methods. Fuzzy Sets and Systems, 105 (1995), 171-182.
- [13] R. S. SUTTON and A. G. BARTO: Reinforcement learning: An introduction. Bradford Books. MIT Press, Cambridge, Mass., 1998.
- [14] K. S. TANG, K. M. MAN and G. CHEN: A G-A optimized fuzzy PD+I controller for nonlinear systems. Proc. Int. Conf on Industrial Electronics, Control, and Instrumentation, Denver, USA, (2001), 718-723.
- [15] K. S. TANG, K. M. MAN and G. CHEN: An optimal fuzzy PID controller. IEEE Trans. Industrial electronics, 48 (2001), 757-765.
- [16] H. A. VAROL and Z. BINGUL: A new PID tuning technique using Ant algorithm. Proc. American Control Conf, Boston, Massachusetts, USA, (2004), 2154-2159.
- [17] C. J. C. H. WATKINS: Learning from delayed rewards. PhD Thesis, University of Cambridge, England, 1989.
- [18] C. J. C. H. WATKINS and P. DAYAN: Technical note, Q-learning. Machine Learning, 8 (1992), 279-292.
- [19] C.-J. Wu, T.-L. LEE, Y.-Y. Fu and L.-C. LAI: Auto-tuning fuzzy PID control of a pendubot system. Proc. 4th IEEE Int. Conf. on Mechatronics, Kumamoto, Japan, (2007), 1-6.
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Typ dokumentu
Bibliografia
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bwmeta1.element.baztech-article-BSW3-0048-0008