Skip to main content
Springer Nature Link
Log in

Fuzzy Controllers for Autonomous Mobile Robots

  • Chapter
Springer Handbook of Computational Intelligence

Part of the book series: Springer Handbooks ((SHB))

Abstract

This chapter addresses the tracking problem for the dynamic model of a unicycle mobile robot. A novel optimization method inspired from the chemical reactions is applied to solve this motion problem by integrating a kinematic and a torque controller based on fuzzy logic theory. Computer simulations are presented confirming that this optimization paradigm is able to outperform other optimization techniques applied to this particular robot

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Abbreviations

ACO:

ant colony optimization

CRA:

chemical reaction algorithm

DNA:

deoxyribonucleic acid

FLC:

fuzzy logic controller

FOU:

footprint of uncertainty

GA:

genetic algorithm

MF:

membership function

PSO:

particle swarm optimization

References

  1. N.-Y. Shi, C.-P. Chu: A molecular solution to the hitting-set problem in DNA-based supercomputing, Inf. Sci. 180, 1010–1019 (2010)

    Article  Google Scholar 

  2. L. Yamamoto: Evaluation of a catalytic search algorithm, Proc. 4th Int. Workshop Nat. Inspired Coop. Strateg. Optim., NICSO 2010 (2010) pp. 75–87

    Google Scholar 

  3. T. Meyer, L. Yamamoto, W. Banzhaf, C. Tschudin: Elongation control in an algorithmic chemistry, Lect. Notes Comput. Sci. 5777, 273–280 (2010)

    Article  Google Scholar 

  4. J. Xu, A.Y.S. Lam, V.O.K. Li: Chemical reaction optimization for the grid scheduling problem, IEE Commun. Soc., ICC 2010 (2010) pp. 1–5

    Google Scholar 

  5. Y. Kanayama, Y. Kimura, F. Miyazaki, T. Noguchi: A stable tracking control method for a non-holonomic mobile robot, Proc. IEEE/RSJ Int. Workshop Intell. Robot. Syst., Osaka (1991) pp. 1236–1241

    Chapter  Google Scholar 

  6. T.-C. Lee, C.H. Lee, C.-C. Teng: Tracking control of mobile robots using the backsteeping technique, Proc. 5th Int. Conf. Contr. Automat. Robot. Vis., Singapore (1998) pp. 1715–1719

    Google Scholar 

  7. T.-C. Lee, K. Tai: Tracking control of unicycle-modeled mobile robots using a saturation feedback controller, IEEE Trans. Control Syst. Techn. 9(2), 305–318 (2001)

    Article  Google Scholar 

  8. S. Bentalba, A. El Hajjaji, A. Rachid: Fuzzy control of a mobile robot: A new approach, IEEE Int. Conf. Control Appl., Hartford (1997) pp. 69–72

    Google Scholar 

  9. S. Ishikawa: A method of indoor mobile robot navigation by fuzzy control, Proc. Int. Conf. Intell. Robot. Syst., Osaka (1991) pp. 1013–1018

    Google Scholar 

  10. T.H. Lee, F.H.F. Leung, P.K.S. Tam: Position control for wheeled mobile robot using a fuzzy controller, 25th Annu. Conf. IEEE, San Jose (1999) pp. 525–528

    Google Scholar 

  11. S. Pawlowski, P. Dutkiewicz, K. Kozlowski, W. Wroblewski: Fuzzy logic implementation in mobile robot control, 2nd Workshop Robot Motion Control (2001) pp. 65–70

    Google Scholar 

  12. C.-C. Tsai, H.-H. Lin, C.-C. Lin: Trajectory tracking control of a laser-guided wheeled mobile robot, Proc. IEEE Int. Conf. Control Appl., Taipei (2004) pp. 1055–1059

    Google Scholar 

  13. S.V. Ulyanov, S. Watanabe, V.S. Ulyanov, K. Yamafuji, L.V. Litvintseva, G.G. Rizzotto: Soft computing for the intelligent robust control of a robotic unicycle with a new physical measure for mechanical controllability, Soft Comput. 2, 73–88 (1998)

    Article  Google Scholar 

  14. R. Fierro, F.L. Lewis: Control of a nonholonomic mobile robot using neural networks, IEEE Trans. Neural Netw. 9(4), 589–600 (1998)

    Article  Google Scholar 

  15. K.T. Song, L.H. Sheen: Heuristic fuzzy-neural Network and its application to reactive navigation of a mobile robot, Fuzzy Sets Syst. 110(3), 331–340 (2000)

    Article  Google Scholar 

  16. A.M. Bloch, S. Drakunov: Tracking in non-holonomic dynamic system via sliding modes, Proc. IEEE Conf. Decis. Control, Brighton (1991) pp. 1127–1132

    Google Scholar 

  17. D. Chwa: Sliding-mode tracking control of nonholonomic wheeled mobile robots in polar coordinates, IEEE Trans. Control Syst. Tech. 12(4), 633–644 (2004)

    MathSciNet  Google Scholar 

  18. R. Fierro, F.L. Lewis: Control of a nonholonomic mobile robot: Backstepping kinematics into dynamics, Proc. 34th Conf. Decis. Control, New Orleans (1995) pp. 3805–3810

    Google Scholar 

  19. T. Fukao, H. Nakagawa, N. Adachi: Adaptive tracking control of a non-holonomic mobile robot, IEEE Trans. Robot. Autom. 16(5), 609–615 (2000)

    Article  Google Scholar 

  20. A.R. Sahab, M.R. Moddabernia: Backstepping method for a single-link flexible-joint manipulator using genetic algorithm, IJICIC 7(7B), 4161–4170 (2011)

    Google Scholar 

  21. J. Yu, Y. Ma, B. Chen, H. Yu, S. Pan: Adaptive neural position tracking control for induction motors via backstepping, IJICIC 7(7B), 4503–4516 (2011)

    Google Scholar 

  22. L. Astudillo, O. Castillo, L. Aguilar: Intelligent control for a perturbed autonomous wheeled mobile robot: A type-2 fuzzy logic approach, Nonlinear Stud. 14(1), 37–48 (2007)

    MathSciNet  MATH  Google Scholar 

  23. R. Martinez, O. Castillo, L. Aguilar: Optimization of type-2 fuzzy logic controllers for a perturbed autonomous wheeled mobile robot using genetic algorithms, Inf. Sci. 179(13), 2158–2174 (2009)

    Article  MATH  Google Scholar 

  24. O. Castillo, R. Martinez-Marroquin, P. Melin, J. Soria: Comparative study of bio-inspired algorithms applied to the optimization of type-1 and type-2 fuzzy controllers for an autonomous mobile robot, Stud. Comput. Intell. 256, 247–262 (2009)

    Article  Google Scholar 

  25. I. Kolmanovsky, N.H. McClamroch: Developments in nonholonomic nontrol problems, IEEE Control Syst. Mag. 15, 20–36 (1995)

    Article  Google Scholar 

  26. G. Campion, G. Bastin, B. D'Andrea-Novel: Structural properties and classification of kinematic and dynamic models of wheeled mobile robots, IEEE Trans. Robot. Autom. 12(1), 47–62 (1996)

    Article  Google Scholar 

  27. W. Nelson, I. Cox: Local path control for an autonomous vehicle, Proc. IEEE Conf. Robotics Autom. (1988) pp. 1504–1510

    Google Scholar 

  28. S. Oh, H. Jang, W. Pedrycz: A comparative experimental study of type-1/type-2 fuzzy cascade controller based on genetic algorithms and particle swarm optimization, Expert Syst. Appl. 38(9), 11217–11229 (2011)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Tijuana Institute of Technology, Calzada Tecnolo-gico s/n, 22379, Tijuana, Mexico

    Oscar Castillo

  2. Dep. Computer Science, Tijuana Institute of Technology, CA 91909, Chula Vista, USA

    Patricia Melin

Authors
  1. Patricia Melin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Oscar Castillo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Patricia Melin .

Editor information

Editors and Affiliations

  1. Systems Research Inst., Polish Academy of Sciences, ul. Newelska 6, 01-447, Warsaw, Poland

    Janusz Kacprzyk

  2. Dep. Electrical and Computer Engineering, University of Alberta, 116 Street 9107, T6J 2V4, Edmonton, Alberta, Canada

    Witold Pedrycz

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Melin, P., Castillo, O. (2015). Fuzzy Controllers for Autonomous Mobile Robots. In: Kacprzyk, J., Pedrycz, W. (eds) Springer Handbook of Computational Intelligence. Springer Handbooks. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43505-2_80

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-43505-2_80

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-43504-5

  • Online ISBN: 978-3-662-43505-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics