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

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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.

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References

  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):1209–1214

    Article  Google Scholar 

  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)

  3. Esfandyari M, Fanaei MA, Zohreie H (2013) Adaptive fuzzy tuning of PID controllers. J Neural Comput Appl 23(1):19–28

    Article  Google Scholar 

  4. Jin J, Su Y (2005) Improved adaptive genetic algorithm. Comput Eng Appl 29(3):64–70

    Google Scholar 

  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 52:253–270

    Article  Google Scholar 

  6. Ding Y, Ying H, Shao S (2003) Typical Takagi–Sugeno PI and PD fuzzy controllers: analytical structures and stability analysis. Inf Sci 151:245–262

    Article  MATH  MathSciNet  Google Scholar 

  7. Ahn KK, Truong DQ (2009) Online tuning fuzzy PID controller using robust extended Kalman filter. J Process Control 19:1011–1023

    Article  Google Scholar 

  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):2368–2375

    Article  Google Scholar 

  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 2013

  10. Gan SC, Yang PX (2005) PID self-tuning based on fuzzy genetic algorithm. J N China Electric Power Univ 32(5):43–46

    Google Scholar 

  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)

  12. He G, Tan G (2007) An optimal nonlinear PID controller based on ant algorithm. Programmable controller & factory automation, pp 99–105

  13. Kennedy J, Eberhart RC (2001) Swarm intelligence. Morgan Kaufmann Publishers, Los Altos

    Google Scholar 

  14. Gaing ZL (2004) A particle swarm optimization approach for optimum design of PID Controller in AVR system. IEEE Trans Energy Convers 19(2):384–391

  15. Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In: Proceedings IEEE international conference evolution computer, Anchorage, AK, pp 69–73

  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):2251–2259

    Article  Google Scholar 

  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 753–757

  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)

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Authors and Affiliations

  1. INAUT - Universidad Nacional de San Juan (UNSJ - Conicet), Capital, San Juan, Argentina

    F. G. Rossomando & C. M. Soria

Authors
  1. F. G. Rossomando
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  2. C. M. Soria
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Correspondence to F. G. Rossomando.

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Rossomando, F.G., Soria, C.M. Identification and control of nonlinear dynamics of a mobile robot in discrete time using an adaptive technique based on neural PID. Neural Comput & Applic 26, 1179–1191 (2015). https://doi.org/10.1007/s00521-014-1805-8

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  • DOI: https://doi.org/10.1007/s00521-014-1805-8

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