Advertisement

Two new control signal approaches for obtaining the MRAS-CDM and a real-time application

  • Ömür ÖcalEmail author
  • Atilla Bir
  • Bernd Tibken
Article
  • 79 Downloads

Abstract

The coefficient diagram method (CDM) is one of the most effective control design methods. It creates control systems that are very stable and robust with responses without the overshoot and small settling time. Furthermore, all control parameters of the control systems are changed by varying some adjustment parameters in CDM depending on the demands. The model reference adaptive systems (MRAS) are the systems that follow and change the control parameters according to a given model reference system. There are several methods to combine the CDM with MRAS. One of these is to use the MRAS parameters as a gain of the CDM parameters. Another is to directly use the CDM parameters as the MRAS parameters. In the industrial applications, the system parameters can be changed frequently, but if the controller, by self-tuning, recalculates and develops its own parameters continuously, the system becomes more robust. Also, if the poles of the controlled systems approach the jw axis, the response of the closed-loop MRAS becomes more and more insufficient. In order to obtain better results, CDM is combined with a self-tuning model reference adaptive system. Systems controlled by a model reference adaptive controller give responses with small or without overshoot, have small settling times, and are more robust. Thus, in this paper, a hybrid combination of MRAS and CDM is developed and two different control structures of the control signal are investigated. The two methods are compared with MRAS and applied to real-time process control systems.

Keywords

Coefficient diagram method (CDM) coefficient diagram method adaptive control process control model reference adaptive systems (MRAS) 

References

  1. [1]
    M. Comanescu, L. Xu. Sliding-mode MRAS speed estimators for sensorless vector control of induction machine. IEEE Transactions on Industrial Electronics, vol. 53, no. 1, pp. 146–153, 2006.CrossRefGoogle Scholar
  2. [2]
    M. Elbuluk, T. Liu, I. Husain. Neural network-based model reference adaptive systems for high performance motor drives and motion controls. In Proceedings of IEEE Industry Applications Conference, IEEE, Rome, Italy, vol. 2, pp. 959–965, 2000.Google Scholar
  3. [3]
    B. M. Mirkin, P. O. Gutman. Output feedback model reference adaptive control for multi-input-multi-output plants with state delay. Systems & Control Letters, vol. 54, no. 10, pp. 961–972, 2005.MathSciNetzbMATHCrossRefGoogle Scholar
  4. [4]
    B. Y. Zhang, X. J. Chen, G. G. Sun, G. H. Feng. A position sensorless vector-control system based on MRAS for low speed and high torque PMSM drive. In Proceedings of the 8th International Conference on Electrical Machines and Systems, IEEE, Nanjing, PRC, vol. 2, pp. 1682–1686, 2005.CrossRefGoogle Scholar
  5. [5]
    M. Rashed, P. F. A. MacConnell, A. F. Stronach. Nonlinear adaptive state-feedback speed control of a voltage-fed induction motor with varying parameters. IEEE Transactions on Industry Applications, vol. 42, no. 3, pp. 723–732, 2006.CrossRefGoogle Scholar
  6. [6]
    S. Manabe. Brief tutorial and survey of coefficient diagram method. In Proceedings of the 4th Asian Control Conference, Singapore, pp. 1161–1166, 2002.Google Scholar
  7. [7]
    S. Manabe, Y. C. Kim. Recent development of coefficient diagram method. In Proceedings of the 3rd Asian Control Conference, Shanghai, PRC, 2000.Google Scholar
  8. [8]
    S. Manabe. Coefficient diagram method. In Proceedings of the 14th International Federation of Automatic Control Symposium on Automatic Control in Aerospace, Seoul, Korea, pp. 199–210, 1998.Google Scholar
  9. [9]
    M. Koksal, S. E. Hamamci. Robust temperature control of MSF desalination plants with coefficient diagram method. In Proceedings of IEEE Conference on Control Applications, IEEE, vol. 2, pp. 1437–1442, 2003.Google Scholar
  10. [10]
    A. I. Cahyadi, D. Isarakorn, T. Benjanarasuth, J. Ngamwiwit, N. Komine. Application of coefficient diagram method for rotational inverted pendulum control. In Proceedings of the 8th Control, Automation, Robotics and Vision Conference, IEEE, vol. 3, pp. 1769–1773, 2004.Google Scholar
  11. [11]
    N. Khuakoonratt, T. Benjanarasuth, J. Ngamwiwit, N. Komine. I-PDA incorporating FFC control system designed by CDM. In Proceedings of SICE Annual Conference, IEEE, vol. 2, pp. 2250–2254, 2003.Google Scholar
  12. [12]
    D. Kumpanya, T. Benjanarasuth, J. Ngamwiwit, N. Komine. PI controller design with feedforward by CDM for level processes. In Proceedings of TENCON 2000, IEEE, Kuala Lumpur, Malaysia vol. 2, pp. 65–69, 2000.Google Scholar
  13. [13]
    Ö. Öcal, M. T. Söylemez, A. Bir. Robust pole assignment using coefficient diagram method. In Proceedings of International Conference on Automatic Control and Systems Engineering, Cairo, Egypt, pp. 197–202, 2005.Google Scholar
  14. [14]
    Ö. Öcal, M. T. Söylemez, A. Bir. Robust controller tuning based on coefficient diagram method. In Proceedings of International Conference on Control, Manchester, UK, 2008.Google Scholar
  15. [15]
    N. Bigdeli, M. Haeri. Adaptve-CDM: A new AQM controller to cope with TCP/AQM networks dynamics. In Proceedings of the 5th International Conference on Information, Communications and Signal Processing, IEEE, Bangkok, Thailand, pp. 1520–1524, 2005.CrossRefGoogle Scholar
  16. [16]
    N. Bigdeli, M. Haeri. Performance analysis of CDM as an AQM congestion controller in dynamic networks. In Proceedings of International Conference on Computer as a Tool, IEEE, Belgrade, Serbia, vol. 1, pp. 680–683, 2005.CrossRefGoogle Scholar
  17. [17]
    Ö. Öcal, A. Bir. A hybrid real-time control using model reference adaptive system designed by coefficient diagram method. In Proceedings of International Conference on Control, Automation and Systems, Seul, Korea, 2008.Google Scholar
  18. [18]
    A. V. Lipatov. Some necessary and sufficient conditions that polynomials be of Hurwitz type. Differents, Urayn, vol. 12, pp. 2269–2270, 1976.zbMATHGoogle Scholar
  19. [19]
    N. I. Sokolov, A. V. Lipatov. On necessary conditions for stability of linear systems. Moscow Aviation Institute Transactions, vol. 240, pp. 26–30, 1972.Google Scholar
  20. [20]
    K. J. Astrom, B. Witttenmark. Adaptive Control, 2nd ed., New Jersey, USA: Addison-Wesley, 1995.Google Scholar
  21. [21]
    H. P. Wang, Y. T. Liu. Integrated design of speed-sensorless and adaptive controller for a brushless DC motor. IEEE Transactions on Power Electronics, vol. 21, no. 2, pp. 518–523, 2006.CrossRefGoogle Scholar
  22. [22]
    Y. R. Chen, J. Wu, N. C. Cheung. Lyapunov’s stability theory-based model reference adaptive control for permanent magnet linear motor drives. In Proceedings of the 1st International Conference on Power Electronics Systems and Applications, IEEE, pp. 260–266, 2004.Google Scholar
  23. [23]
    T. T. Arif. Adaptive control of rigid body satellite. International Journal of Automation and Computing, vol. 5, no. 3, pp. 296–306, 2008.CrossRefGoogle Scholar
  24. [24]
    X. Y. Luo, Z. H. Zhu, X. P. Guan. Adaptive fuzzy dynamic surface control for uncertain nonlinear systems. International Journal of Automation and Computing, vol. 6, no. 4, pp. 385–390, 2009.CrossRefGoogle Scholar
  25. [25]
    A. Inoue, M. C. Deng. Framework of combined adaptive and non-adaptive attitude control system for a helicopter experimental system. International Journal of Automation and Computing, vol. 3, no. 3, pp. 229–234, 2006.CrossRefGoogle Scholar

Copyright information

© Institute of Automation, Chinese Academy of Sciences and Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.Department of Electrical EngineeringIstanbul Technical UniversityIstanbulTurkey
  2. 2.Department of Electronic EngineeringUniversity of WuppertalWuppertalGermany

Personalised recommendations