Advertisement

A Fuzzy-Based Modified Gain Adaptive Scheme for Model Reference Adaptive Control

  • A. K. Pal
  • Indrajit Naskar
  • Sampa Paul
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 701)

Abstract

In this paper, a fuzzy-based adaptive scheme for model reference adaptive control (MRAC) is proposed. In MRAC, the choice of proper adaptive gain (γ) is a cumbersome job, and it is usually done by trial and error method. To eliminate this shortcoming, here fuzzy logic is incorporated in the control loop to tune the adaptive gain (γ). In design of model reference adaptive control, MIT rule is followed, where a cost function is defined as a function of error between the outputs of the plant and the reference model, and the controller parameters are adjusted in such a way so that this cost function is minimized. The experiments on the different second-order linear/nonlinear systems are illustrated to show the merits of the proposed fuzzy-based model reference adaptive control (FMRAC) scheme over the MRAC. The performances of the proposed control algorithms are evaluated and shown by means of simulation on MATLAB and Simulink.

Keywords

Adaptive control Fuzzy control MIT rule Fuzzy-based MRAC Adaptation gain 

References

  1. 1.
    Benjelloun, K., Mechlih, H., Boukas, E.K.: A modified model reference adaptive control algorithm for DC servomotor. Second IEEE Conf. Control Appl. 2, 941–946 (1993)CrossRefGoogle Scholar
  2. 2.
    Tsai, P.-Y., Huang, H.C., Chen Y.-J., Hwang, R.-C.: The model reference control by auto tuning PID-like fuzzy controller. In: International Conference on Control Applications. Taipei, Taiwan, pp. 32–42 (2004)Google Scholar
  3. 3.
    Astrom, K.J., Bjorn, W.: Adaptive Control, 2nd edn, pp. 185–225. Pearson Education, Asia (2001)Google Scholar
  4. 4.
    Cirrincione, M., Pucci, M.: An MRAS-based sensorless high performance induction motor drive with a predictive adaptive model. IEEE Trans. Ind. Electr. 52(2), 532–542 (2005)CrossRefGoogle Scholar
  5. 5.
    Tayebi, A.: Model reference adaptive iterative learning control for linear systems. Int. J. Adapt. Control Signal Process. 20, 475–489 (2006)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Ehsani, M.S.: Adaptive control of servo motor by MRAC method. In: IEEE International Conference on Vehicle, Power and Propulsion. Arlington, TX, pp. 78–83 (2007)Google Scholar
  7. 7.
    Kersting, S., Martin, B.: Direct and indirect model reference adaptive control for multivariable piecewise affine systems. IEEE Trans. Autom. Control 1–16 (2017)Google Scholar
  8. 8.
    Mushiri, T., Mahachil, A., Mbohwa, C.: A model reference adaptive control (MRAC) system for the pneumatic valve of the bottle washer in beverages using simulink. In: International Conference on Sustainable Materials Processing and Manufacturing, pp. 364–373 (2017)Google Scholar
  9. 9.
    Pal, A.K., Mudi Rajani K.: An adaptive fuzzy controller for overhead crane. In: IEEE International Conference on Advanced Communication Control and Computing Technologies (ICACCCT), pp. 328–332 (2012)Google Scholar
  10. 10.
    Koo, T.J.: Stable model reference adaptive fuzzy control of a class of nonlinear systems. IEEE Trans. Fuzzy Syst. 9(4), 624–636 (2001)CrossRefGoogle Scholar
  11. 11.
    Abid, H., Chtourou, M., Toumi, A.: An indirect model reference robust fuzzy adaptive control for a class of SISO nonlinear systems. Int. J. Control Autom. Syst. 7, 982–991 (2009)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of AEIEHeritage Institute of TechnologyKolkataIndia

Personalised recommendations