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A Fuzzy Control Design for an Autonomous Mobile Robot Using Ant Colony Optimization

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Book cover Recent Advances on Hybrid Approaches for Designing Intelligent Systems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 547))

Abstract

In this chapter we describe the methodology to design an optimized fuzzy logic controller for an autonomous mobile robot, using Ant Colony Optimization (ACO). This is achieved by applying a systematic and hierarchical optimization modifying the conventional ACO algorithm using ants partition. The simulations results proved that the proposed algorithm performs even better that the classic ACO algorithm when optimizing membership functions of FLC, parameters and fuzzy rules.

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References

  1. Brownlee, J.: Clever Algorithms, Nature-Inspired Programming Recipes. Creative Commons Publisher, Melbourne, Australia (2011)

    Google Scholar 

  2. Castillo, O., Neyoy, H., Soria, J., García, M., Valdez, F.: Dynamic fuzzy logic parameter tuning for ACO and its application in the fuzzy logic control of an autonomous mobile robot. Int. J. Adv. Rob. Syst., 10, (2013)

    Google Scholar 

  3. Chang, Y.-H., Chang, C.-W., Lin, H.-W., Tao, C.W.: Fuzzy Controller Design for Ball and Beam System with an Improved Ant Colony Optimization, pp. 616–621. World Academy of Science, Engineering and Technology (2009)

    Google Scholar 

  4. Chaparro, I., Valdez, F.: Variants of Ant Colony Optimization: A Metaheuristic for Solving the Traveling Salesman Problem. Recent Adv. on Hybrid Intell. Syst., 323–331 (2013)

    Google Scholar 

  5. Dorigo, M., Birattari, M., Stützle, T.: Ant colony optimization. IEEE Computational Intelligence Magazine, pp. 28–39 (2006)

    Google Scholar 

  6. Dorigo, M., Stützle, T.: Ant Colony Optimization. Massachusetts Institute of Technology, Bradford, Cambridge (2004)

    Book  MATH  Google Scholar 

  7. Eberhart, R.C., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, pp. 39–43. IEEE Service Center, Piscataway, 1995

    Google Scholar 

  8. Engelbrecht, A.: Fundamentals of Computational Swarm Intelligence, pp. 85–131. Wiley, London (2005)

    Google Scholar 

  9. Galea, M., Shen, Q.: Simultaneous ant colony optimisation algorithms for learning linguistic fuzzy rules. In: Abraham, A., Grosan, C., Ramos, V. (eds.) Swarm Intelligence in Data Mining, pp. 75–99. Springer, Berlin (2006)

    Google Scholar 

  10. Jang, J.-S.R., Mizutani, E., Sun, C.-T.: Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. Prentice Hall, Upper Saddle River (1997)

    Google Scholar 

  11. Lam, H.K., Leung, F.H.F., Tam, P.K.S.: Design of a fuzzy controller for stabilizing a ball-and-beam system, industrial electronics society. In: IECON’99 Proceedings. The 25th Annual Conference of the IEEE, vol. 2, pp. 520–524 (1999)

    Google Scholar 

  12. Langari R., Yen, J., Fuzzy Logic: Intelligence, Control and Information, Center of Fuzzy Logic, Robotics, and Intelligent Systems. Texas A&M University, Prentice Hall, USA (1998)

    Google Scholar 

  13. Lizárraga, E., Castillo, O., Soria, J.: A method to solve the traveling salesman problem using ant colony optimization variants with ant set partitioning. In: Castillo, O., Melin, P., Kacprzyk, J., Pedrycz, W. (eds.) Recent Advances on Hybrid Intelligent Systems (Studies in Computational Intelligence), pp. 237–247. Springer, Berlin (2013)

    Google Scholar 

  14. Melendez, A., Castillo, O.: Evolutionary optimization of the fuzzy integrator in a navigation system for a mobile robot. In: Recent Advances on Hybrid Intelligent Systems, pp. 21–31. Springer, Berlin (2013)

    Google Scholar 

  15. Melin, P., Olivas, F., Castillo, O., Valdez, F., Soria, J., García, J.: Optimal design of fuzzy classification systems using PSO with dynamic parameter adaptation through fuzzy logic. Expert Syst. Appl. 40(8), 3196–3206 (2013)

    Article  Google Scholar 

  16. Mezura-Montes, E., Palomeque-Ortiz, A.: Self-adaptive and Deterministic Parameter Control in Differential Evolution for Constrained Optimization. Efren Mezura-Montes, Laboratorio Nacional de Informática Avanzada (LANIA A.C.), Rébsamen 80, Centro, Xalapa, Veracruz, 91000, Mexico (2009)

    Google Scholar 

  17. Naredo, E., Castillo, O.: ACO-tuning of a fuzzy controller for the ball and beam problem. MICAI 2, 58–69 (2011)

    Google Scholar 

  18. Mendel, J.: Uncertain Rule-Based Fuzzy Logic Systems, Introduction and new directions, PH PTR (2001)

    Google Scholar 

  19. Neyoy, H., Castillo, O., Soria, J.: Dynamic fuzzy logic parameter tuning for ACO and its application in TSP problems. In: Kacprzyk, J. Recent Advances on Hybrid Intelligent Systems (Studies in Computational Intelligence), pp. 259–273. Springer, Berlin (2013)

    Google Scholar 

  20. Olivas, F., Castillo, O.: Particle swarm optimization with dynamic parameter adaptation using fuzzy logic for benchmark mathematical functions, pp. 247–258. Recent Advances on Hybrid Intelligent Systems. Springer, Berlin (2013)

    Google Scholar 

  21. Stützle, T., Hoos, H.H.: MAX-MIN ant system and local search for combinatorial optimization problems. In: Voss, S., Martello, S., Osman, I., Roucairol, C. (eds.) Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimization, pp. 137–154. Kluwer Academic Publishers, Dordrecht (1999)

    Google Scholar 

  22. Stützle, T., Hoos, H.H.: MAX-MIN ant system. Future Generation Comput. Syst. 16(8), 889–914 (2000)

    Article  Google Scholar 

  23. Stützle, T., Linke, S.: Experiments with variants of ant algorithms. Mathw. Soft. Comput. 9(2–3), 193–207 (2002)

    MATH  Google Scholar 

  24. Stützle, T., Hoos, H.H.: Improving the ant system: a detailed report on the MAXMIN ant system. Technical report AIDA-96-12, FG Intellektik, FB Informatik. TU Darmstadt, Germany (1996)

    Google Scholar 

  25. Tzen, S-T.: GA approach for designing fuzzy control with nonlinear ball-and-beam. The International Conference on Electrical Engineering, pp. 05–09. Japan (2008)

    Google Scholar 

  26. Yen, J., Langari, R.: Fuzzy Logic: Intelligence, Control and Information. Prentice Hall, Upper Saddle River (2003)

    Google Scholar 

  27. Zadeh, L.A.: Fuzzy Logic. IEEE Computer, pp. 338–353. (1965)

    Google Scholar 

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Authors and Affiliations

  1. Tijuana Institute of Technology, Tijuana, Mexico

    Evelia Lizarraga, Oscar Castillo, José Soria & Fevrier Valdez

Authors
  1. Evelia Lizarraga
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  2. Oscar Castillo
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  3. José Soria
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  4. Fevrier Valdez
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Corresponding author

Correspondence to Fevrier Valdez .

Editor information

Editors and Affiliations

  1. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Mexico

    Oscar Castillo

  2. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Mexico

    Patricia Melin

  3. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, Canada

    Witold Pedrycz

  4. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

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Lizarraga, E., Castillo, O., Soria, J., Valdez, F. (2014). A Fuzzy Control Design for an Autonomous Mobile Robot Using Ant Colony Optimization. In: Castillo, O., Melin, P., Pedrycz, W., Kacprzyk, J. (eds) Recent Advances on Hybrid Approaches for Designing Intelligent Systems. Studies in Computational Intelligence, vol 547. Springer, Cham. https://doi.org/10.1007/978-3-319-05170-3_20

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  • DOI: https://doi.org/10.1007/978-3-319-05170-3_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-05169-7

  • Online ISBN: 978-3-319-05170-3

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