Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Intelligent nonlinear optimal controller of a biotechnological process

Wybrane pełne teksty z tego czasopisma
Warianty tytułu
Języki publikacji
Designing an effective criterion and learning algorithm for finding the best structure is a major problem in the control design process. In this paper, the fuzzy Proportional Parallel Distributed Compensation with Reduced Rule Base approach (PPDC_RRB) is proposed. The design problem considered is essentially nonlinear optimal and robust control problem due to the nonlinear nature of the Takagi-Sugeno fuzzy system. The control signal thus obtained will minimize performance index, which is a function of the tracking/regulating errors, the quantity of the energy of the control signal applied to the system, and the number of fuzzy rules. The genetic learning is proposed for constructing the PPDC_RRB controller. The chromosome genes are arranged into two parts, the binary-coded part contains the control genes and the real-coded part contains the genes parameters representing the fuzzy knowledge base. The effectiveness of this chromosome formulation enables the fuzzy sets and rules to be optimally reduced. The chaotic mutation is introduced for maintaining the population diversity during the evolution process of the genetic algorithm. The performances of the PPDC_RRB are compared with those found by the traditional PD controller with Genetic Optimization (PD_GO). Simulations demonstrate that the proposed PPDC_RRB and PD_GO has successfully met the design specifications.
Opis fizyczny
Bibliogr. 24 poz., rys.
  • [1] CHENG-Wu CHEN: Stability conditions of fuzzy systems and its application to structural and mechanical systems. Advances in Engineering Software, 37 (2006), 624-629.
  • [2] L. A. ZADEH: Fuzzy sets. Information and Control, 8 (1965), 338-353.
  • [3] L. A. ZADEH: Soft computing and fuzzy logie. IEEE Software, November (1994) 48-56.
  • [4] T. TAKAGI, M. SUGENO: Fuzzy identification of systems and its applications to modelling and control. IEEE Trans. Systems Man Cybernet, 15(1), (1985), 116-132.
  • [5] KWEE-BO SIM, KWANG-SUB BYUN and DONG-WOOK LEE: Design a fuzzy controller using schema coevolutionary algorithm. IEEE Trans, on Fuzzy Systems, 12(4), (2004), 565-568.
  • [6] DAVID E. GOLDBERG: Algorithmes genetiąues : exploration, optimisation et ap-prentissage automatiąue. Addison-Wesley, 1994.
  • [7] YONMOOK PARK, MIN-JEA TAHK and HYOCHOONG BANG: Design and analysis of optimal controller for fuzzy systems with input constraint. IEEE Trans, on Fuzzy Systems, 12(6), (2004), 766-779.
  • [8] SIMANT R. UPRETI: A new robust technique for optimal control of chemical en-gineering processes. Computer and Chemical Engineering, 28 (2004), 1325-1336.
  • [9] M. J. ER and D.H. LIN: A new approach for stabilizing nonlinear systems with time delays. Int. J. of Intelligent Systems, 17 (2002), 289-302.
  • [10] XIANFU ZHANG, ZHAOLIN CHENG and QNGRONG Liu: A fuzzy logie approach to optimal control of nonlinear time-delay systems. Proc. 5th World Congress on Intelligent Control and Automation, June 15-19, 2004, Hangzhou, P.R. China, 902-906.
  • [11] BIN-DA LIU, CHUEN-YAU CHEN and Ju-YING TSAO: Design of adaptive fuzzy logie controller based on Linguistic-Hedge concepts and genetic algorithms. IEEE Trans, on Systems, Man, and Cybernetics-Part B: Cybernetics, 31(1), (2001), 32-53.
  • [12] GONG-YOU TANG, YAN-DONG ZHAO and BAO-LIN ZHANG: Optimal output tracking control for nonlinear systems via successive approximation approach. Nonlinear Analysis, 66 (2007), 1365-1377.
  • [13] K. TANAKA and M. SUGENO: Stability analysis and design of fuzzy control systems. Fuzzy Sets and Systems, 45(2), (1992), 135-156.
  • [14] K. BELARBI, F. TITEL, W. BOUREBIA and K. BENMAHAMMED: Design of Mamdani fuzzy logie controllers with rule base minimisation using genetic algorithm. Engineering Applications ofArtificial Intelligence, 18(7), (2005), 875-880.
  • [15] CHI-HO LEE, MING YUCHI and JONG-HWAN KIM: TWO phase optimization of fuzzy controller by evolutionary programming". The 2003 Congress on Evolutio-nary Computation, 3, 8-12 December (2003), 1949-1956.
  • [16] R. ALCALEA, J. ALCALEA-FDEZ, J. CASILLAS, O. CORDEON and F. HER-RERA: Hybrid learning models to get the interpretability-accuracy trade-off in fuzzy modelling. Soft Computing, 10 (2006), 717-734.
  • [17] R. L. HAUPT and S.E. HAUPT: Practical genetic algorithms. Second Edition. WI-LEY, 2004.
  • [18] S. K. SHARMA and G.W. IRWIN: Fuzzy coding of genetic algorithms. IEEE Trans.on 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.
  • [20] ZHI-HUA CUI, JIAN-CHAO ZENG and YU-BIN Xu: Dynamie circle nonlinear genetic algorithm. Proc. Second Int. Conf. on Machinę Learning and Cybernetics, Xi'an, 2-5 November (2003), 1836-1840.
  • [21] You YONG, SHENG WANXING and WANG SUNAM: Study of chaos genetic algorithms and its application in neural networks. IEEE Region 10 Conf. on Computers, Communications, Control and Power Engineering, 1 (2002), 232-235.
  • [22] R. MAY: Simple mathematical models with very complicated dynamics. Naturę, 261(1976), 459-467.
  • [23] XUEFENG F. YAN, DEZHAO Z. CHEN and SHANGXU X. Hu: Chaos-genetic algorithms for optimizing the operating conditions based on RBF-PLS model. Computer and Chemical Engineering, 27 (2003), 1393-1404.
  • [24] Y. OYSAL, Y BECERIKLI and A. FERIT KONAR: Modified descend curvature based fixed form fuzzy optimal control of nonlinear dynamical systems. Computer and Chemical Engineering, 30 (2006), 878-888.
Typ dokumentu
Identyfikator YADDA
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.