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Neural Network-Based Adaptive Dynamic Surface Control for Inverted Pendulum System

  • Enping Wei
  • Tieshan Li
  • Junfang Li
  • Yancai Hu
  • Qiang Li
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 215)

Abstract

In this paper, a novel neural network (NN)-based adaptive dynamic surface control (DSC) is proposed for inverted pendulum system. This scheme overcomes the problem of “explosion of complexity” which is inherent in the traditional backstepping technique. Meanwhile, the effect of input saturation constrains is considered in the control design. All the signals in the closed-loop system are proved uniformly ultimately bounded. Finally, the experimental platform simulation results are used to demonstrate the effectiveness of the proposed scheme.

Keywords

Inverted pendulum system Neural network Dynamic surface control Input saturation 

Notes

Acknowledgments

This work is supported in part by the National Natural Science Foundation of China (Grant No. 51179019, 60874056), the Natural Science Foundation of Liaoning Province (Grant No. 20102012), the Program for Liaoning Excellent Talents in University (LNET) (Grant No. LR2012016), and the Fundamental Research Funds for the Central Universities (2011QN097).

References

  1. 1.
    Spong MW, Corke P, Lozano R (2001) Nonlinear control of the inertia wheel pendulum. Automatica 37(11):1845–1851CrossRefMATHGoogle Scholar
  2. 2.
    Alberto I (1985) Nonlinear control systems, 2nd edn. Springer, BerlinGoogle Scholar
  3. 3.
    Sontag ED, Sussman HJ (1988) Further comments on the stability of the angular velocity of a rigid body. Syst Control Lett 12:213–217Google Scholar
  4. 4.
    Swaroop D, Gerdes JC, Yip PP, Hedrick JK (1997) Dynamic surface control of nonlinear systems. Proc Am Control Conf 5:3028–3034Google Scholar
  5. 5.
    Wonand M, Hedrick JK (1996) Multiple surface sliding control of a class of uncertain nonlinear systems. Int J Control 64(4):693–706CrossRefGoogle Scholar
  6. 6.
    Gao WZ, Selmic RR (2006) Neural network control of a class of nonlinear systems with actuator saturation. IEEE Trans Neural Netw 17(1):147–156CrossRefMATHGoogle Scholar
  7. 7.
    Wang D, Huang J (2005) Neural network-based adaptive dynamic surface control for a class of uncertain nonlinear systems in strict-feedback form. IEEE Trans Neural Netw 16(1):195–202CrossRefGoogle Scholar
  8. 8.
    Gutman S (1979) Uncertain dynamical system—A Lyapunov min max approach. IEEE Trans Automat Control AC-24:437–443Google Scholar
  9. 9.
    Yang Y, Feng G, Ren J (2004) A combined backstepping and small-gain approach to robust adaptive fuzzy control for strict-feedback nonlinear systems. IEEE Trans Syst Man Cybern 34(3):1596–1601Google Scholar
  10. 10.
    Zhou J, Er MJ, Zhou Y (2006) Adaptive neural network control of uncertain nonlinear systems in the presence of input saturation. In: Proceeding of the 9th international conference on control, automation, Robotics and vision, ICARCV ‘06, pp 1–5Google Scholar
  11. 11.
    Qu Z (1998) Robust control of nonlinear uncertain systems. Wiley, New YorkMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Enping Wei
    • 1
  • Tieshan Li
    • 1
  • Junfang Li
    • 1
  • Yancai Hu
    • 1
  • Qiang Li
    • 1
  1. 1.Navigaion CollegeDalian Maritime UniversityDalianChina

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