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Neural Computing and Applications

, Volume 26, Issue 5, pp 1179–1191 | Cite as

Identification and control of nonlinear dynamics of a mobile robot in discrete time using an adaptive technique based on neural PID

  • F. G. RossomandoEmail author
  • C. M. Soria
Original Article

Abstract

In this work, original results, concerning the application of a discrete-time adaptive PID neural controller in mobile robots for trajectory tracking control, are reported. In this control strategy, the exact dynamical model of the robot does not need to be known, but a neural network is used to identify the dynamic model. To implement this strategy, two controllers are implemented separately: a kinematic controller and an adaptive neural PID controller. The uncertainty and variations in the robot dynamics are compensated by an adaptive neural PID controller. It is efficient and robust in order to achieve a good tracking performance. The stability of the proposed technique, based on the discrete-time Lyapunov's theory, is proven. Finally, experiments on the mobile robot have been developed to show the performance of the proposed technique, including the comparison with a classical PID.

Keywords

MIMO system Neural networks Nonlinear control Adaptive control 

References

  1. 1.
    Normey-Rico EJ, Alcalá I, Gómez-Ortega J, Camacho EF (2001) Mobile robot path tracking using a robust PID controller. Control Eng Pract 9(11):1209–1214CrossRefGoogle Scholar
  2. 2.
    Padhy PK, Sasaki T, Nakamura S, Hashimoto H (2010) Modelling and position control of mobile robot. Advanced motion control, 2010 11th IEEE international workshop on, pp 100–105, March (2010)Google Scholar
  3. 3.
    Esfandyari M, Fanaei MA, Zohreie H (2013) Adaptive fuzzy tuning of PID controllers. J Neural Comput Appl 23(1):19–28CrossRefGoogle Scholar
  4. 4.
    Jin J, Su Y (2005) Improved adaptive genetic algorithm. Comput Eng Appl 29(3):64–70Google Scholar
  5. 5.
    Precup R-E, Preitl S, Faur G (2003) PI predictive fuzzy controllers for electrical drive speed control: methods and software for stable development. Comput Ind 52:253–270CrossRefGoogle Scholar
  6. 6.
    Ding Y, Ying H, Shao S (2003) Typical Takagi–Sugeno PI and PD fuzzy controllers: analytical structures and stability analysis. Inf Sci 151:245–262zbMATHMathSciNetCrossRefGoogle Scholar
  7. 7.
    Ahn KK, Truong DQ (2009) Online tuning fuzzy PID controller using robust extended Kalman filter. J Process Control 19:1011–1023CrossRefGoogle Scholar
  8. 8.
    Lu W, Yang JH, Liu XD (2012) The PID controller based on the artificial neural network and the differential evolution algorithm. J Comput 7(10):2368–2375CrossRefGoogle Scholar
  9. 9.
    Mahmud K (2013) Neural network based PID control analysis. In: Global high tech congress on electronics (GHTCE), 2013 IEEE, pp 141, 145, 17–19 Nov 2013Google Scholar
  10. 10.
    Gan SC, Yang PX (2005) PID self-tuning based on fuzzy genetic algorithm. J N China Electric Power Univ 32(5):43–46Google Scholar
  11. 11.
    Mahony TO, Downing CJ, Fatla K (2000) Genetic algorithm for PID parameter optimization: minimizing error criteria. In: Process control and instrumentation, 26–28 July 2000, pp 148–153. University of Stracthclyde (2000)Google Scholar
  12. 12.
    He G, Tan G (2007) An optimal nonlinear PID controller based on ant algorithm. Programmable controller & factory automation, pp 99–105Google Scholar
  13. 13.
    Kennedy J, Eberhart RC (2001) Swarm intelligence. Morgan Kaufmann Publishers, Los AltosGoogle Scholar
  14. 14.
    Gaing ZL (2004) A particle swarm optimization approach for optimum design of PID Controller in AVR system. IEEE Trans Energy Convers 19(2):384–391Google Scholar
  15. 15.
    Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In: Proceedings IEEE international conference evolution computer, Anchorage, AK, pp 69–73Google Scholar
  16. 16.
    Rossomando FG, Soria C, Carelli R (2013) Adaptive neural sliding mode compensator for a class of nonlinear systems with unmodeled uncertainties. Eng Appl Artif Intell 26(10):2251–2259CrossRefGoogle Scholar
  17. 17.
    Li Y, Wang Z, Zhu L (2010) Adaptive neural network PID sliding mode dynamic control of nonholonomic mobile robot. In: IEEE international conference on information and automation (ICIA), pp 753–757Google Scholar
  18. 18.
    Carelli, R. and De La Cruz C. Dynamic Modeling and Centralized Formation Control of Mobile Robots. In: 32nd Annual conference of the IEEE industrial electronics society IECON, Paris (2006)Google Scholar

Copyright information

© The Natural Computing Applications Forum 2014

Authors and Affiliations

  1. 1.INAUT - Universidad Nacional de San Juan (UNSJ - Conicet), CapitalSan JuanArgentina

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