Skip to main content
SpringerLink
Log in

Design and Performance Analysis of a Modified MRAC for Second-Order Integrating Processes

  • Conference paper
  • First Online:
Computational Intelligence, Communications, and Business Analytics (CICBA 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 775))

  • 838 Accesses

Abstract

For the processes having time varying nature performance of conventional PID controller is not satisfactory. In such cases adaptive controllers are suitable as they are capable to modify the control action according to the changes of process dynamics and undesired load variations. In model reference adaptive control (MRAC), desired process response is provided with the choice of a reference model. Adaptation algorithm changes the control action according to the difference between the outputs of the actual process and reference model with a fixed adaptation gain. This paper deals with a modified MRAC technique designed for the second-order integrating processes with dead-time. In the proposed modified MRAC scheme, proportional-derivative (PD) feedback is incorporated in the conventional MRAC structure and the final modified controller is termed as MRAC-PD. It ensures improved response compared to conventional MRAC. Enhanced set-point tracking with fewer oscillations and lesser oscillations during undesired load changes substantiate the superiority of the proposed MRAC-PD controller compared to conventional MRAC and this fact is also reflected in the calculated values of performance indices – integral absolute error (IAE) and integral time absolute error (ITAE) under closed loop operation.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

References

  1. Seborg, D.E., Edgar, T.F., Mellichamp, D.A.: Process Dynamic and Control, 2nd edn. Wiley, New York (2004)

    Google Scholar 

  2. Bequett, B.W.: Process Control Modelling and Simulation. Prentice Hall, New Jersy (2003)

    Google Scholar 

  3. Astrom, K.J., Wittenmark, B.: Adaptive Control, 2nd edn. Dover Publications (2006)

    Google Scholar 

  4. Ljung, L.: Perspectives on system identification. In: Annual Reviews in Control, vol. 34, no.1 (2010)

    Google Scholar 

  5. Nath, U.M., Datta, S., Dey, C.: Centralized auto-tuned IMC-PI controllers forindustrial coupled tank process with stability analysis. In: Second IEEE Conference on Recent Trends in Information Systems, pp. 296–301(2015)

    Google Scholar 

  6. Wang, X., Zhao, J., Tang, Y.: State tracking model reference adaptive control for switched nonlinear systems with linear uncertain parameters. J. Control Theor. Appl. 10(3), 354–358 (2012)

    Article  MathSciNet  Google Scholar 

  7. Benjelloun, K., Mechlih, H., Boukas, E.K.: An modified model reference adaptive control algorithm for DC servomotor. In: Second IEEE Conference on Control Applications, Vancouver, B.C., pp. 941–946 (1993)

    Google Scholar 

  8. Singh, B., Kumar, V.: Design and simulation of auto tuning of PID controller using MRAC technique for coupled tank system. Int. J. Sci. Res. 4(5), 3037–3040 (2015)

    Article  Google Scholar 

  9. Feng, G.: A survey on analysis and design of model-based fuzzy control systems. IEEE Trans. Fuzzy Syst. 14(5), 676–697 (2006)

    Article  Google Scholar 

  10. Ram, A.G., Lincoln, A.: A model reference – based fuzzy adaptive PI controller for non-linear level process system. Int. J. Recent Res. Appl. Stud. 14(2), 477–486 (2013)

    Google Scholar 

  11. Prakash, R., Anita, R.: Neuro- PI controller based model reference adaptive control for nonlinear systems. Int. J. Eng. Sci. Technol. 3(6), 44–60 (2011)

    Google Scholar 

  12. 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 

  13. Swarnkar, P., Jain, S., Nema, R.K.: Effect of adaptation gain in model reference adaptive controlled second order system. Eng. Technol. Appl. Sci. Res. 1(3), 70–75 (2011)

    Google Scholar 

  14. Zareh, M., Soheili, S.: A modified model reference adaptive control with application to MEMS gyroscope. J. Mech. Sci. Technol. 25(8), 2061–2066 (2011)

    Article  Google Scholar 

  15. Ogata, K.: Modern Control Enginering, 5th edn. Prentice Hall, Upper Saddle River (2010)

    Google Scholar 

  16. Gaeta, A.D., Montanaro, U.: Application of a robust model reference adaptive control algorithm to a nonlinear automotive actuator. Int. J. Autom. Comput. 11(4), 377–391 (2014)

    Article  Google Scholar 

  17. Selfridge, J.M., Tao, G.: Multivariable output feedback MRAC for a Quadrotor UAV. In: 2016 American Control Conference (2016)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Heritage Institute of Technology, Kolkata, India

    Reshma Sengupta

  2. University of Calcutta, Kolkata, India

    Chanchal Dey

Authors
  1. Reshma Sengupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chanchal Dey
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Reshma Sengupta .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Kalyani, Kalyani, West Bengal, India

    J. K. Mandal

  2. Department of Computer and System Sciences, Visva Bharati University, Bolpur Santiniketan, West Bengal, India

    Paramartha Dutta

  3. Department of Information Technology, Calcutta Business School, Kolkata, India

    Somnath Mukhopadhyay

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Nature Singapore Pte Ltd.

About this paper

Cite this paper

Sengupta, R., Dey, C. (2017). Design and Performance Analysis of a Modified MRAC for Second-Order Integrating Processes. In: Mandal, J., Dutta, P., Mukhopadhyay, S. (eds) Computational Intelligence, Communications, and Business Analytics. CICBA 2017. Communications in Computer and Information Science, vol 775. Springer, Singapore. https://doi.org/10.1007/978-981-10-6427-2_37

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-6427-2_37

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6426-5

  • Online ISBN: 978-981-10-6427-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation