Nonlinear adaptive controller applied to an Antilock Braking System with parameters variations

  • Cuauhtémoc Acosta Lúa
  • Stefano Di Gennaro
  • María Eugenia Sánchez Morales
Regular Papers Control Theory and Applications
First Online:
Received:
Revised:
Accepted:

Abstract

The control of an Antilock Braking System is a difficult problem, due to its nonlinear dynamics and to the uncertainties in its characteristics and parameters. To overcome these issues, in this work an adaptive controller is proposed. The controller is designed under the assumption that the friction coefficient is unknown, and further perturbing frictions act on the system. Finally, the convergence to an ε-ball of the origin is proved when these perturbing parameters vary. The performance of the nonlinear dynamic controllers is evaluated by some experimental tests on a mechatronic system representing a quarter-car model. The results show how the controller ensures the tracking of the desired reference and identifies the unknown parameters.

Keywords

Adaptive control antilock braking system nonlinear control real-time simulation 
This is a preview of subscription content, log in to check access.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    R. Emig, H. Goebels, and J. Schramm, “Antilock braking systems (ABS) for commercial vehicles-status 1990 and future prospects,” Proc. of the International Transportation Electronics Congress on Vehicle Electronics in the 90’s, pp. 515–523, October 1990.CrossRefGoogle Scholar
  2. [2]
    S. Zhen-Jun, Z. Tian-Jun, and Z. Hong-Yan, “Research on road friction coefficient estimation algorithm based on extended Kalman filter,” Proc. of the International Intelligent Computation Technology and Automation (ICICTA) Conference, pp. 418–422, October 2008.Google Scholar
  3. [3]
    C. H. Lin and H. Chun, “Self-learning fuzzy sliding mode control for antilock braking systems,” IEEE Trans. on Control System Technology, vol. 11, no. 2, pp. 273–278, March 2003. [click]CrossRefGoogle Scholar
  4. [4]
    J. Song, “Integrated control of break pressure and rearwheel steering to improve lateral stability with fuzzy logic,” International Journal of Automotive Technology, vol. 13, no. 4, pp. 563–570, June 2012. [click]CrossRefGoogle Scholar
  5. [5]
    W. K. Lennon and K. M. Passino, “Intelligent control for brake system,” IEEE Trans. on Control System Technology, vol. 7, no. 2, pp. 188–202, March 1999. [click]CrossRefGoogle Scholar
  6. [6]
    K. Han, Y. Hwang, E. Lee, and S. Choi, “Robust estimation of maximum tire-road friction coefficient considering road surface irregularity,” International Journal of Automotive Technology, vol. 17, no. 3, pp. 415–425, June 2016. [click]CrossRefGoogle Scholar
  7. [7]
    H. Chun-Fei and K. Tzu-Chun, “Adaptive exponentialreaching sliding-mode control for antilock braking systems,” Nonlinear Dyn, vol. 77, no. 3, pp. 993–1010, August 2014.CrossRefGoogle Scholar
  8. [8]
    User’s manual, The Laboratory Antilock Braking System Controlled from PC, Inteco Ltd, Poland, 2006.Google Scholar
  9. [9]
    M. Hussin Al-Mola, M. Mailah, P. M. Samin, A. H. Muhaimin, and M.Y. Abdullah, “Performance comparison between sliding mode control and active force control for a nonlinear anti-lock brake system,” WSEAS Trans. on System and Control, vol. 9, pp. 101–107, January 2014.Google Scholar
  10. [10]
    Y. Oniz, E. Kayacan, and O. Kaynak, “A dynamic method to forecast the wheel slip for antilock braking system and its experimental evaluation,” IEEE Trans. on System, Man and Cybernetics-Part B: Cybernetics, vol. 39, no. 2, pp. 551–560, April 2009. [click]CrossRefGoogle Scholar
  11. [11]
    E. Kayacan, Y. Oniz, and O. Kaynak, “A Grey system modeling approach for sliding-mode control of antilock braking system,” IEEE Trans. on Industrial Electronics, vol. 56, no. 8, pp. 3244–3252, August 2009. [click]CrossRefGoogle Scholar
  12. [12]
    R. E. Precup, S. V. Spataru, M. B. Radac, E. M. Petriu, S. Preitl, and R.C. David, “Experimental results of modelbased fuzzy control solutions for a laboratory antilock braking system,” Human Computer Systems Interaction: Backgrounds and Applications, Springer, 2012.Google Scholar
  13. [13]
    M. B. Radac, R. E. Precup, S. Preitl, J. K. Tar, and E. M. Petriu, “Gain-scheduling and iterative feedback tuning of PI controllers for longitudinal slip control,” Proc. of the 6th IEEE International Conference on Computational Cybernetics, pp. 183–188, November 2008. [click]Google Scholar
  14. [14]
    R. Precup, S. Preitl, M. Radac, E. M. Petriu, C. Dragos, and J. K. Tar, “Experimental-based teaching in advanced control engineering,” IEEE Trans. on Education, vol. 61, no. 6, pp. 345–355, August 2011.CrossRefGoogle Scholar
  15. [15]
    M. Habibi and A. Yazdizadeh, “A new fuzzy logic road detector for antilock braking system application,” IEEE International Conference on Control and Automation, pp. 1–6, June 2010.Google Scholar
  16. [16]
    M. A. Khanesar, E. Kayacan, M. Teshnehlab, and O. Kaynak, “Extended Kalman filter based learning algorithm for type-2 fuzzy logic systems and its experimental evaluation,” IEEE Trans. on Industrial Electronics, vol. 59, no. 11, November 2012.Google Scholar
  17. [17]
    A. V. Topalov, E. Kayacan, Y. Oniz, and O. Kaynak, “Adaptive neuro-fuzzy control with sliding mode learning algorithm: application to antilock braking system,” Proc. of the 7th Asian Control Conference, pp. 1–6, August 2009.Google Scholar
  18. [18]
    A. V. Topalov, E. Kayacan, Y. Oniz, and O. Kaynak, “Neuro-fuzzy control of antilock braking system using variable structure-systems based learning algorithm,” Proc. of the International Conference on Adaptive and Intelligent Systems, pp. 1–6, September 2009.Google Scholar
  19. [19]
    A. V. Topalov, Y. Oniz, E. Kayacan, and O. Kaynak, “Neuro-fuzzy control of antilock braking system using sliding mode incremental learning algorithm,” Neurocomputing, pp. 1883–1893, May 2011. [click]Google Scholar
  20. [20]
    S. V. Emelyanov, S. K. Korovin, and L.V. Levantovsky, “Higher order sliding regimes in the binary control systems,” Soviet Physics, vol. 31, no. 4, pp. 291–293, 1986.Google Scholar
  21. [21]
    A. Dadashnialehi, A. Bab-Hadiashar, Z. Cao, and A. Kapoor, “Intelligent sensorless antilock braking system for brushless in-wheel electric vehicle,” IEEE Trans. on Industrial Electronics, vol. 62, no. 3, pp. 1629–1638, March 2015. [click]CrossRefGoogle Scholar
  22. [22]
    A. Dadashnialehi, A. Bab-Hadiashar, Z. Cao, and A. Kapoor, “Intelligent sensorless ABS for in-wheel electric vehicle,” IEEE Trans. on Industrial Electronics, vol. 61, no. 4, pp. 1957–1969, April 2014. [click]CrossRefGoogle Scholar
  23. [23]
    C. Acosta Lua, S. Di Gennaro, B. Castillo-Toledo, and M. Martinez-Gardea, “Dynamic control applied to a laboratory antilock braking system,” Mathematical Problems in Engineering, vol. 2015, pp. 1–10, January 2015.MathSciNetCrossRefGoogle Scholar
  24. [24]
    M. Martinez-Gardea, I. J. Mares Guzmán, C. A. Lúa, S. Di Gennaro, and I. V. Álvarez, “Design of a nonLinear observer for a laboratory antilock braking system,” Journal of Control Engineering and Applied Informatics (CEAI), vol. 17, no. 3, pp. 105–112, 2015.Google Scholar
  25. [25]
    M. Martinez-Gardea, I. J. Mares-Guzman, S. Di Gennaro, and C. Acosta Lúa, “Experimental comparison of linear and nonlinear controllers applied to an antilock braking system,” IEEE Multiconference on System Control, pp. 71–76, December 2014.Google Scholar
  26. [26]
    M. Martinez-Gardea, C. Acosta-Lúa, I. Vázquez-Álvarez, and S. Di Gennaro, “Event-triggered linear control design for an antilock braking system,” Proc. of the IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC), pp. 1–6, November 2015.Google Scholar
  27. [27]
    H. B. Pacejka, Tyre and Vehicle Dynamics, Elsevier Butterworth-Hein, 2006.MATHGoogle Scholar
  28. [28]
    L. Zhao, Z. Liu, and H. Chen, “Design of a nonlinear observer for vehicle velocity estimation and experiments,” IEEE Trans. on Control Systems Technology, vol. 19, no.3, 664–672, May 2011.CrossRefGoogle Scholar
  29. [29]
    R. Bosch GmbH, ABS-A Success Story, 2003. http://www.bosch.com/.Google Scholar
  30. [30]
    R. B. Kenneth, “Reference input wheel slip tracking using sliding mode control,” SAE Technical Paper Series, 2002-01-0301.Google Scholar
  31. [31]
    A. Levant, “Higher-order sliding modes, differentiation and output feedback control,” International Journal of Control, vol. 76, no. 9, pp. 924–941, November 2010. [click]MathSciNetMATHGoogle Scholar

Copyright information

© Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Cuauhtémoc Acosta Lúa
    • 1
  • Stefano Di Gennaro
    • 2
  • María Eugenia Sánchez Morales
    • 1
  1. 1.Departamento de Ciencias TecnológicasUniversidad de Guadalajara, Centro Universitario de la CiénegaOcotlánMéxico
  2. 2.Department of Information Engineering, Computer Science and Mathematics, and with the Center of Excellence DEWSUniversity of L’AquilaL’AquilaItaly

Personalised recommendations