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Experimental Results of Model-Based Fuzzy Control Solutions for a Laboratory Antilock Braking System

  • R. E. Precup
  • S. V. Spătaru
  • M. B. Rădac
  • E. M. Petriu
  • S. Preitl
  • C. A. Dragoş
  • R. C. David
Chapter
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 99)

Abstract

This chapter presents aspects concerning the design of model-based fuzzy control solutions dedicated to the longitudinal slip control of an Antilock Braking System laboratory equipment. Continuous-time and discrete-time Takagi-Sugeno (T-S) fuzzy models of the controlled process are first derived on the basis of the modal equivalence principle. The consequents of the T-S models of the T-S fuzzy controllers are local state feedback controllers which are solutions to several linear quadratic regulator (LQR) problems and the parallel distributed compensation is next applied. Linear matrix inequalities are solved to guarantee the global stability of the discrete-time fuzzy control systems and to give the optimal state feedback gain matrices of the LQR problems. A set of real-time experimental results is included to validate the new fuzzy control solutions.

Keywords

Linear Matrix Inequality Fuzzy Control Fuzzy Controller Linguistic Term Linear Quadratic Regulator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. [Grzymała-Busse et al. 2005]
    Grzymała-Busse, J.W., Hippe, Z.S., Mroczek, T., et al.: Data mining analysis of granular bed caking during hop extraction. In: Proc. 5th International Conference on Intelligent Systems Design and Applications, Wroclaw, Poland, pp. 426–431 (2005)Google Scholar
  2. [Kouro et al. 2010]
    Kouro, S., Malinowski, M., Gopakumar, K., et al.: Recent advances and industrial applications of multilevel converters. IEEE Trans. Ind. Electron 58(8), 2553–2580 (2010)CrossRefGoogle Scholar
  3. [Kulikowski 2009]
    Kulikowski, J.L.: Decision making supported by fuzzy deontological statements. In: Proc. Int. Multiconference on Computer Science and Information Technology IMCSIT 2009, Mrągowo, Poland, pp. 65–73 (2009)Google Scholar
  4. [Li et al. 2010]
    Li, F.Z., Hu, R.F., Yao, H.X.: The performance of automobile antilock brake system based on fuzzy robust control. In: Proc. 2010 Int. Conf. on Intelligent Computation Technology and Automation, Changsha, China, vol. 3, pp. 870–873 (2010)Google Scholar
  5. [Oniz et al. 2009]
    Oniz, Y., Kayacan, E., Kaynak, O.: A dynamic method to forecast the wheel slip for antilock braking system and its experimental evaluation. IEEE Trans. Syst. Man Cybern. B Cybern. 39(2), 551–560 (2009)CrossRefGoogle Scholar
  6. [Precup et al. 2010]
    Precup, R.E., Spătaru, S.V., Rădac, M.B., et al.: Model-based fuzzy control solutions for a laboratory antilock braking system. In: Proc. 3rd Int. Conf. on Human System Interaction, Rzeszow, Poland, pp. 133–138 (2010)Google Scholar
  7. [Rădac et al. 2009]
    Rădac, M.B., Precup, R.E., Preitl, S., et al.: Tire slip fuzzy control of a laboratory anti-lock braking system. In: Proc. European Control Conf., Budapest, Hungary, pp. 940–945 (2009)Google Scholar
  8. [Ridluan et al. 2009]
    Ridluan, A., Manic, M., Tokuhiro, A.: EBaLM-THP - artificial neural network thermo-hydraulic prediction tool for an advanced nuclear components. Nucl. Eng. Des. 239(2), 308–319 (2009)CrossRefGoogle Scholar
  9. [Wang et al. 2009]
    Wang, W.Y., Li, I.H., Chen, M.C., et al.: Dynamic slip-ratio estimation and control of antilock braking systems using an observer-based direct adaptive fuzzy-neural controller. IEEE Trans. Ind. Electron 56(5), 1746–1756 (2009)CrossRefGoogle Scholar
  10. [Wilamowski 2009]
    Wilamowski, B.M.: Neural network architectures and learning algorithms. IEEE Ind. Electron Mag. 3(4), 56–63 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • R. E. Precup
    • 1
  • S. V. Spătaru
    • 2
  • M. B. Rădac
    • 1
  • E. M. Petriu
    • 3
  • S. Preitl
    • 1
  • C. A. Dragoş
    • 1
  • R. C. David
    • 1
  1. 1.Department of Automation and Applied Informatics“Politechnica” University of TimisoaraTimisoaraRomania
  2. 2.Department of Energy TechnologyAalborg UniversityAalborg EastDenmark
  3. 3.School of Information Technology and EngineeringUniversity of OttawaOttawaCanada

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