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Multiobjective optimization of a fuzzy PID controller

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Języki publikacji
EN
Abstrakty
EN
A fuzzy logic controller with multilayer neutral network whose synaptic weights represent the fuzzy knowledge base and its application to the highly nonlinear systems is presented in this work. The scaling factors of the input variables, membership functions and the rule sets are optimized by the use of the multiobjective genetic algorithms. The fuzzy network structure is specified by a combination of the mixed Takagi-Sugeno's and Mamdani's fuzzy reasoning. The mixed, Binary-Real-Integer, optimal coding is utilized to construct the chromosomes, which define the same of necessary prevai;ling parameters for the conception of the desired controller. This new controller stands out by a non-standard gain, which varies lineary with the fazzy inputs. Under certain conditions, it becomes similar to the conventional PID controller with non-linearly variable coefficients. Computer simulation indicates that the designed fuzzy controller is satisfactory in control of a nonlinear system "Inverted Pendulum".
Rocznik
Strony
445--461
Opis fizyczny
Bibliogr. 19 poz., rys., tab.
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autor
autor
Bibliografia
  • [1] L. A. ZADEH: Fuzzy sets. Information and Control, 8 (1965), 338-353.
  • [2] N. XIONG and L. LITZ: A new genetic based approach to fuzzy controller design and its application. Proc. IEEE Mt. Conf on Control Applications, Triest, Italy, (1998), 937-941.
  • [3] T. G. B. AMARAL, M. M. CRISOSTOMO and V. F. PIRES: Adaptive neuro-fuzzy inference system for modelling and control. Proc. First Int. IEEE Symp. Intelligent Systems, (2002), 67-72.
  • [4] Z. J. ZHOU, Z. Y. MAO and P. K. S. TAM: On designing an optimal fuzzy neural network controller using genetic algorithms. Proc. 3rd World Congress on Intelligent Control and Automation, Hafei, P.R. China, (2000).
  • [5] M. RUSSO Genetic fuzzy learning. IEEE Trans. Evolutionary Computation 4(3), (2000).
  • [6] L. REN and Y. DING: Parameter optimisation for a class of general TS fuzzy controllers via a new DNA-based genetic algorithm. Proc. 5th World Congress on Intelligent Control and Automation, Hangzhou, P. R. China, (2004), 2149-2153.
  • [7] J. GODJEVAC: Neuro-fuzzy controllers: design and application. Presses polytechniques et universitaires Romandes, Lausanne, Collection META, 1997.
  • [8] D. E. GOLDBERG: Algorithmes genetiques: exploration, optimisation et apprentissage automatique, Addison-Wesley, 1994.
  • [9] T. TAKAGI and M. SUGENO:Fuzzy identification of systems and applications to modelling and control. IEEE Trans. Systems, Man, and Cybernetics, 15(1), (1985), 116-132.
  • [10] H. YING: Constructing nonlinear variable gain controllers via the Takagi-Sugeno fuzzy control. IEEE Trans. Fuzzy Systems, 6(2), (1998), 226-234.
  • [11] H. YING: Theory and application of a novel fuzzy PID controller using a simplified Takagi-Sugeno rule scheme. Information Sciences, 123 (2000), 281-293.
  • [12] Y. DING, H. YING and S. SHAG: Typical Takagi-Sugeno PI and PD fuzzy controllers: Analytical structures and stability analysis. Information Sciences, 151 (2003), 245-262.
  • [13] Z. XIU and G. REN: Optimisation design of TS-PID controllers based on genetic algorithms. Proc. 5th World Congress on Intelligent Control and Automation, Hangzhou, P. R. China, (2004), 2476-2480.
  • [14] P. J. ESCAMILLA-AMBROSIO and N. MORT: A novel design and tuning procedure for PID type fuzzy logic controllers. Proc. First Int. IEEE Symp. 'Intelligent Systems', 2002, 36-41.
  • [15] S.-K. OH, WITOLD PEDRYCZ, S.-B. RHO and TAE-CHON AHN: Parameter estimation of fuzzy controller and its application to inverted pendulum. Engineering Applications of Artificial Intelligence, 17 (2004), 37-60.
  • [16] A. HOMAIFAR and E. MCCORMICK: Simultaneous design of membership functions and rules sets for fuzzy controllers using genetic algorithms. IEEE Trans. Fuzzy Systems, 3(2), (1995), 161-176.
  • [17] J. KIM and B. P. ZEIGLER: Designing fuzzy logic controllers using a multiresolutional search paradigm. IEEE Trans. Fuzzy Systems, 4(3), (1996), 213-226.
  • [18] S. K. SHARMA and G. W. IRWIN: Fuzzy coding of genetic algorithms. IEEE Trans. Evolutionary Computation, 7(4), (2003), 344-355.
  • [19] K. L. LO and M. O. SADEGH: Systematic method for the design of a full-scale fuzzy PID controller for SVC to control power system stability. IEE Proc. Gener. Transm. Distrib., 150(3), (2003), 297-304.
Typ dokumentu
Bibliografia
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bwmeta1.element.baztech-article-BSW3-0031-0006
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