Implementation a Fuzzy System for Trajectory Tracking of an Omnidirectional Mobile Autonomous Robot

  • Jacinto González-Aguilar
  • Oscar CastilloEmail author
  • Prometeo Cortés-AntonioEmail author
Part of the Studies in Computational Intelligence book series (SCI, volume 827)


This document presents a problem of tracking lines of an omnidirectional mobile robot, using a fuzzy proportional control system and classical proportional control to compare the result achieved by each control. It is implemented in the Robotino mobile platform, two digital optical sensors are used to control the direction of the angular velocity of the robot and an inductive analog sensor to control the linear velocity in x \( v_{x} \). For the analysis of the system, Robotino SIM and Simulink is used, which is an additional Matlab tool that allows graphic representation by means of blocks, both linear and non-linear systems. Tests and comparisons are made where the best performance of the fuzzy proportional controller is appreciated.


Proportional control (p) Integral control (I) Derivative control (D) Fuzzy control Robotino 



The authors would like to express thank to the Consejo Nacional de Ciencia y Tecnologia and Tecnológico Nacional de México/Tijuana Institute of Technology for the facilities and resources granted for the development of this research.


  1. 1.
    K.J. Åström, W.K. Ho, Control theory and applications, in IEE proceedings-D, vol. 138, no. 2 (Institution of Electrical Engineers, marzo 1980)Google Scholar
  2. 2.
    F.G. Rossomando, C.M. Soria, Identification and control of nonlinear dynamics of a mobile robot in discrete time using an adaptive technique based on neural PID. Neural Comput. Appl. 26(5), 1179–1191 (2015)CrossRefGoogle Scholar
  3. 3.
    M. Pena et al., Fuzzy logic for omni directional mobile platform control displacement using FPGA and bluetooth. IEEE Lat. Am. Trans. 13(6), 1907–1914 (2015)CrossRefGoogle Scholar
  4. 4.
    C.-C. Tsai, X.-C. Wang, F.-C. Tai, C.-C. Chan, Fuzzy decentralized EIF-based pose tracking for autonomous omnidirectional mobile robot, in 2014 International Conference on Machine Learning and Cybernetics (2014), pp. 748–754Google Scholar
  5. 5.
    R. Choomuang, Hybrid Kalman filter/fuzzy logic based position control of autonomous mobile robot. Int. J. Adv. Robot. Syst. 2, 197–208 (2005)CrossRefGoogle Scholar
  6. 6.
    E.H. Mamdani, N. Baaklini, Prescriptive method for deriving control policy in a fuzzy-logic controller. Electron. Lett. 11(25–26), 625 (1975)CrossRefGoogle Scholar
  7. 7.
    M. Wada, S. Mori, Holonomic and omnidirectional vehicle with conventional tires, in Proceedings of IEEE International, and undefined 1996, (1996)Google Scholar
  8. 8.
    M. Masmoudi, N. Krichen, M. Masmoudi, N. Derbel, Fuzzy logic controllers design for omnidirectional mobile robot navigation. Appl. Soft Comput. J. 49, 901–919 (2016)CrossRefGoogle Scholar
  9. 9.
    S. Oltean, M. Dulau, Position control of Robotino mobile robot using fuzzy logic, in IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR), Cluj-Napoca, Romania, May 2010Google Scholar
  10. 10.
    T. Kalmár-Nagy. Real-time trajectory generation for omnidirectional vehicles, in Proceedings of the American Control Conference (2002), pp. 286–291Google Scholar
  11. 11.
    C. Leal Ramírez, O. Castillo, P. Melin, A. Rodríguez Díaz, Simulation of the bird age-structured population growth based on an interval type-2 fuzzy cellular structure. Inf. Sci. 181(3), 519–535 (2011)MathSciNetCrossRefGoogle Scholar
  12. 12.
    N.R. Cázarez-Castro, L.T. Aguilar, O. Castillo, Designing type-1 and type-2 fuzzy logic controllers via Fuzzy Lyapunov synthesis for nonsmooth mechanical systems. Eng. Appl. AI 25(5), 971–979 (2012)CrossRefGoogle Scholar
  13. 13.
    O. Castillo, P. Melin, Intelligent systems with interval type-2 fuzzy logic. Int. J. Innov. Comput. Inf. Control 4(4), 771–783 (2008)Google Scholar
  14. 14.
    G.M. Mendez, O. Castillo. Interval type-2 TSK fuzzy logic systems using hybrid learning algorithm, in The 14th IEEE International Conference on Fuzzy Systems. FUZZ’05 (2005), pp. 230–235Google Scholar
  15. 15.
    Claudia I. González, Patricia Melin, Juan R. Castro, Olivia Mendoza, Oscar Castillo, An improved sobel edge detection method based on generalized type-2 fuzzy logic. Soft. Comput. 20(2), 773–784 (2016)CrossRefGoogle Scholar
  16. 16.
    Emanuel Ontiveros, Patricia Melin, Oscar Castillo, High order α-planes integration: a new approach to computational cost reduction of General Type-2 Fuzzy Systems. Eng. Appl. AI 74, 186–197 (2018)CrossRefGoogle Scholar
  17. 17.
    P. Melin, O. Castillo, Intelligent control of complex electrochemical systems with a neuro-fuzzy-genetic approach. IEEE Trans. Ind. Electron. 48(5), 951–955 (2001)CrossRefGoogle Scholar
  18. 18.
    E. Rubio, O. Castillo, F. Valdez, P. Melin, C. I. González, G. Martinez. An extension of the fuzzy possibilistic clustering algorithm using type-2 fuzzy logic techniques. Adv. Fuzzy Syst. 2017, 7094046:1–7094046:23 (2017)CrossRefGoogle Scholar
  19. 19.
    Patricia Melin, Alejandra Mancilla, Miguel Lopez, Olivia Mendoza, A hybrid modular neural network architecture with fuzzy Sugeno Integration for time series forecasting. Appl. Soft Comput. 7(4), 1217–1226 (2007)CrossRefGoogle Scholar
  20. 20.
    P. Melin, O. Castillo, Modelling, Simulation and Control of Non-Linear Dynamical Systems: An Intelligent Approach Using Soft Computing and Fractal Theory (CRC Press, Boca Raton, 2001)CrossRefGoogle Scholar
  21. 21.
    Patricia Melin, Daniela Sánchez, Oscar Castillo, Genetic optimization of modular neural networks with fuzzy response integration for human recognition. Inf. Sci. 197, 1–19 (2012)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Tijuana Institute of TechnologyTijuanaMexico

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