Hybrid Control Scheme for Tracking Performance of a Flexible system

  • F. M. Aldebrez
  • M. S. Alam
  • M. O. Tokhi


This paper introduces a hybrid control scheme comprising a proportional and derivative (PD)-like fuzzy controller cascaded with a proportional, integral and derivative (PID) compensator. The hybrid control scheme is developed and implemented for tracking control of the vertical movement of a twin rotor multi-input multi-output system in the hovering mode. The proposed control scheme is designed in a way that the output of the PD-type fuzzy controller is fed as a proportional gain of the PID compensator. Genetic algorithm (GA) is used to tune simultaneously the other two parameters of the PID compensator.

The performance of the proposed hybrid control strategy is compared with a PD-type fuzzy controller and conventional PID compensator in terms of setpoint tracking. It is found that the proposed control strategy copes well over the complexities of the plant and has done better than the other two controllers. The GA optimization technique is also found to be effective and efficient in tuning the PID parameters.


Flexible systems fuzzy control genetic algorithms hybrid control 


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  1. 1.
    Aldebrez F. M., Alam M. S., Tokhi M. O. and Shaheed M. H. (2004) Genetic modelling and vibration control of a twin rotor system. Proceedings of UKACC International Conference on Control-2004, Bath, 6–9 September.Google Scholar
  2. 2.
    Chipperfield A. J. and Fleming P. J. (1994) Parallel Genetic Algorithms: a Survey, Research report no. 518, Department of Automatic Control and Systems Engineering, The University of Sheffield, UK.Google Scholar
  3. 3.
    Feedback Instruments Ltd. (1996) Twin Rotor MIMO System Manual 33-007-0. Sussex, UK.Google Scholar
  4. 4.
    Goldberg, D. E. (1989). Genetic algorithms in search, optimisation and machine learning, Addison Wesley Longman, Publishing Co. Inc., New York.Google Scholar
  5. 5.
    Kumbla K.K. and Jamshidi M. (1994) Control of Robotic Manipulator Using Fuzzy Logic. Proceedings of the Third IEEE Conference on Fuzzy Systems, vol. 1, pp. 518–523.CrossRefGoogle Scholar
  6. 6.
    Lee C. C. (1990) Fuzzy logic in control systems: Fuzzy logic controller— Part I&II. IEEE Trans. Sys., Man, Cybern., vol. SMC-20, no. 2, pp. 404–435.CrossRefGoogle Scholar
  7. 7.
    Mamdani E. H. (1974). Application of Fuzzy Algorithms for Control of Simple Dynamic Plant. Proceedings of IEEE, vol. 121, no. 12, pp. 1585–1588.Google Scholar
  8. 8.
    Mudi R. K. and Pal N. R. (1999) A robust self-tuning scheme for PI and PD-type fuzzy controllers. IEEE Trans. On Fuzzy Systems, vol. 7, no. 1, pp. 2–16.CrossRefGoogle Scholar
  9. 9.
    Pedrycz W. (1991) Fuzzy Modelling: Fundamentals, Construction, and Evaluation. Fuzzy Sets and Systems, vol. 41, no. 1, pp. 1–15.MathSciNetzbMATHCrossRefGoogle Scholar
  10. 10.
    Sugeno M. (Ed.) (1985). Industrial applications of fuzzy control. Elsevier Science, North-Holland, Amsterdam.Google Scholar
  11. 11.
    Verbruggen H. B. and Bruijin P. M. (1997) Fuzzy control and conventional control: what is (and can be) the real contribution of fuzzy systems? Fuzzy Sets and Systems, vol. 90, pp. 151–160.MathSciNetzbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • F. M. Aldebrez
    • 1
  • M. S. Alam
    • 1
  • M. O. Tokhi
    • 1
  1. 1.Department of Automatic Control and Systems EngineeringThe University of SheffieldUK

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