Intelligent Control of Nonlinear Dynamic Plants Using a Hierarchical Modular Approach and Type-2 Fuzzy Logic

  • Leticia Cervantes
  • Oscar Castillo
  • Patricia Melin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7095)

Abstract

In this paper we present simulation results that we have at this moment with a new approach for intelligent control of non-linear dynamical plants. First we present the proposed approach for intelligent control using a hierarchical modular architecture with type-2 fuzzy logic used for combining the outputs of the modules. Then, the approach is illustrated with two cases: aircraft control and shower control and in each problem we explain its behavior. Simulation results of the two case show that proposed approach has potential in solving complex control problems.

Keywords

Granular computing Type-2 fuzzy logic Fuzzy control Genetic Algorithm 

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References

  1. 1.
    Abusleme, H., Angel, C.: Fuzzy control of an unmanned flying vehicle, Ph.d. Thesis, Ponti-ficia University of Chile (2000)Google Scholar
  2. 2.
    Bargiela, A., Wu, P.: Granular Computing: An Introduction. Kluwer Academic Publish, Dordercht (2003)CrossRefMATHGoogle Scholar
  3. 3.
    Bargiela, A., Wu, P.: The roots of Granular Computing. In: GrC 2006, pp. 806–809. IEEE (2006) Google Scholar
  4. 4.
    Blakelock, J.: Automatic Control of Aircraft and Missiles. Prentice-Hall (1965)Google Scholar
  5. 5.
    Coley, A.: An Introduction to Genetic Algorithms for Scientists and Engineers. World Scientific (1999)Google Scholar
  6. 6.
    The 2011 IEEE Internaional Confenrece on Granular Computing Sapporo, GrC 2011, Japan, August 11-13. IEEE Computer Society (2011) Google Scholar
  7. 7.
    Dorf, R.: Modern Control Systems. Addison-Wesley Pub. Co. (1997) Google Scholar
  8. 8.
    Dwinnell, J.: Principles of Aerodynamics. McGraw-Hill Company (1929)Google Scholar
  9. 9.
    Engelen, H., Babuska, R.: Fuzzy logic based full-envelope autonomous flight control for an atmospheric re-entry spacecraft. Control Engineering Practice Journal 11(1), 11–25 (2003)CrossRefGoogle Scholar
  10. 10.
    Federal Aviation Administration, Airplane Flying Handbook, U.S. Department of Transportation Federal Aviation Administration (2007) Google Scholar
  11. 11.
    Federal Aviation Administration. Pilot’s Handbook of Aeronautical Knowledge, U.S. Department of Transportation Federal Aviation Administration (2008) Google Scholar
  12. 12.
    Gardner A.: U.S Warplanes The F-14 Tomcat, The Rosen Publishing Group (2003) Google Scholar
  13. 13.
    Gibbens, P., Boyle, D.: Introductory Flight Mechanics and Performance. University of Sydney, Australia. Paper (1999)Google Scholar
  14. 14.
    Goedel, K.: The Consistency of the Axiom of Choice and of the Generalized Continuum Hypothesis with the Axioms of Set Theory. Princeton University Press, Princeton (1940)Google Scholar
  15. 15.
    Haupt R., Haupt S.: Practical Genetic Algorithm. Wiley-Interscience (2004) Google Scholar
  16. 16.
    Holmes, T.: US Navy F-14 Tomcat Units of Operation Iraqi Freedom, Osprey Publishing Limited (2005) Google Scholar
  17. 17.
    Jamshidi, M., Vadiee, N., Ross, T.: Fuzzy Logic and Control: Software and Hardware Applications, vol. 2. Prentice-Hall, University of New Mexico (1993)Google Scholar
  18. 18.
    Kadmiry, B., Driankov, D.: A fuzzy flight controller combining linguistic and model based fuzzy control. Fuzzy Sets and Systems Journal 146(3), 313–347 (2004)MathSciNetCrossRefMATHGoogle Scholar
  19. 19.
    Karnik, N., Mendel, J.: Centroid of a type-2 fuzzy set. Information Sciences 132, 195–220 (2001)MathSciNetCrossRefMATHGoogle Scholar
  20. 20.
    Keviczky, T., Balas, G.: Receding horizon control of an F-16 aircraft: A comparative study. Control Engineering Practice Journal 14(9), 1023–1033 (2006)CrossRefGoogle Scholar
  21. 21.
    Liu, M., Naadimuthu, G., Lee, E.S.: Trayectory tracking in aircraft landing operations management using the adaptive neural fuzzy inference system. Computers & Mathematics with Applications Journal 56(5), 1322–1327 (2008)MathSciNetCrossRefMATHGoogle Scholar
  22. 22.
    McLean D.: Automatic Flight Control System. Prentice Hall (1990) Google Scholar
  23. 23.
    McRuer, D., Ashkenas, I., Graham, D.: Aircraft Dynamics and Automatic Control. Princeton University Press (1973)Google Scholar
  24. 24.
    Melin, P., Castillo, O.: Intelligent control of aircraft dynamic systems with a new hybrid neuro- fuzzy–fractal Approach. Journal Information Sciences 142(1) (May 2002)Google Scholar
  25. 25.
    Melin, P., Castillo, O.: Adaptive intelligent control of aircraft systems with a hybrid approach combining neural networks, fuzzy logic and fractal theory. Journal of Applied Soft computing 3(4) (December 2003)Google Scholar
  26. 26.
    Mendel, J.: Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions. Prentice-Hall, Upper Saddle River (2001)MATHGoogle Scholar
  27. 27.
    Mitchell, M.: An Introduction to Genetic Algorithms. Massachusetts Institute of Technology (1999)Google Scholar
  28. 28.
    Morelli, E.A.: Global Nonlinear Parametric Modeling with Application to F-16 Aerodynamics, NASA Langley Research Center, Hampton, Virginia (1995) Google Scholar
  29. 29.
    Nelson, R.: Flight Stability and automatic control, 2nd edn. Department of Aerospace and Mechanical Engineering, University of Notre Dame., McGraw Hill (1998)Google Scholar
  30. 30.
    Pedrycz, W., Skowron, A., et al.: Handbook granular computing. Wiley Interscience, New York (2008)CrossRefGoogle Scholar
  31. 31.
    Rachman, E., Jaam, J., Hasnah, A.: Non-linear simulation of controller for longitudinal control augmentation system of F-16 using numerical approach. Information Sciences Journal 164(1-4), 47–60 (2004)CrossRefMATHGoogle Scholar
  32. 32.
    Reiner, J., Balas, G., Garrard, W.: Flight control design using robust dynamic inversion and time- scale separation. Automatic Journal 32(11), 1493–1504 (1996)CrossRefMATHGoogle Scholar
  33. 33.
    Sanchez, E., Becerra, H., Velez, C.: Combining fuzzy, PID and regulation control for an autonomous mini-helicopter. Journal of Information Sciences 177(10), 1999–2022 (2007)CrossRefGoogle Scholar
  34. 34.
    Sefer, K., Omer, C., Okyay, K.: Adaptive neuro-fuzzy inference system based autonomous flight control of unmanned air vehicles. Expert Systems with Applications Journal 37(2), 1229–1234 (2010)CrossRefGoogle Scholar
  35. 35.
    Song, Y., Wang, H.: Design of Flight Control System for a Small Unmanned Tilt Rotor Air-craft. Chinese Journal of Aeronautics 22(3), 250–256 (2009)MathSciNetCrossRefGoogle Scholar
  36. 36.
    Walker, D.J.: Multivariable control of the longitudinal and lateral dynamics of a fly by wire helicopter. Control Engineering Practice 11(7), 781–795 (2003)CrossRefGoogle Scholar
  37. 37.
    Wu, D.: A Brief Tutorial on Interval Type-2 Fuzzy Sets and Systems (July 22, 2010)Google Scholar
  38. 38.
    Wu, D., Jerry, M.: On the Continuity of Type-1 and Interval Type-2 Fuzzy Logic Systems. IEEE T. Fuzzy Systems 19(1), 179–192 (2011)CrossRefGoogle Scholar
  39. 39.
    Zadeh, L.A.: Some reflections on soft computing, granular computing and their roles in the conception, design and utilization of information/intelligent systems. Soft Comput. 2, 23–25 (1998)CrossRefGoogle Scholar
  40. 40.
    Zadeh, L.A.: Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans. Syst. Man Cybern. SMC-3, 28–44 (1973)MathSciNetCrossRefMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Leticia Cervantes
    • 1
  • Oscar Castillo
    • 1
  • Patricia Melin
    • 1
  1. 1.Tijuana Institute of TechnologyMexico

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