Advertisement

RBF NN-Based Backstepping Control for Strict Feedback Block Nonlinear System and Its Application

  • Yunan Hu
  • Yuqiang Jin
  • Pingyuan Cui
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3174)

Abstract

Based on neural networks, a robust control design method is proposed for strict-feedback block nonlinear systems with mismatched uncertainties. Firstly, Radial-Basis-Function (RBF) neural networks are used to identify the nonlinear parametric uncertainties of the system, and the adaptive tuning rules for updating all the parameters of the RBF neural networks are derived using the Lyapunov stability theorem to improve the approximation ability of RBF neural networks on-line. Considering the known information, neural network and robust control are used to deal with the design problem when control coefficient matrices are unknown and avoid the possible singularities of the controller. For every subsystem, a nonlinear tracking differentiator is introduced to solve the “computer explosion” problem in backstepping design. It is proved that all the signals of the closed-loop system are uniform ultimate bounded.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Sastry, S.S., Isidori, A.: Adaptive Control of Linearizable System. IEEE Translations on Automatic Control 11, 1123–1131 (1989)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Seto, D., Annaswamy, A.M., Baillieul, J.: Adaptive Control of Nonlinear Systems with a Triangular Structure. IEEE Translations on Automatic Control 7, 1411–1428 (1994)CrossRefMathSciNetGoogle Scholar
  3. 3.
    Polycarpou, M.M.: Stable Adaptive Neural Control Scheme for Nonlinear Systems. IEEE Translations on Automatic Control 3, 447–451 (1996)CrossRefMathSciNetGoogle Scholar
  4. 4.
    Krstic, M., Kanellakopoulos, I., Kokotovic, P.: Nonlinear and Adptive Control Design. Wiley–Interscience Publication, Chichester (1995)Google Scholar
  5. 5.
    Vadim, I.U., De-Shiou, C., Hao-Chi, C.: Block Control Principle for Mechanical Systems. Journal of Dynamic Systems, Measurement, and Control 1, 1–10 (2000)Google Scholar
  6. 6.
    Loukianov, A., Toledo, B.C., Dodds, S.J.: Nonlinear Sliding Surface Design in the Presence of Uncertainty. In: Proceedings of the 14th IFAC, Beijing, P.R.China, pp. 55–60 (1999)Google Scholar
  7. 7.
    Jagannathan, S., Lewis, F.L.: Robust Backstepping Control of a Class of Nonlinear Systems Using Fuzzy Logic. Information Sciences 2, 223–240 (2000)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Yan, L., Sundararajan, N., Saratchandran, P.: Neuro-controller Design for Nonlinear Fighter Aircraft Maneuver Using Fully Tuned RBF Networks. Automatica 8, 1293–1301 (2001)Google Scholar
  9. 9.
    Park, J., Sandberg, I.W.: Universal Approximation Using Radial Basis Function Networks. Neural Computation 2, 246–257 (1991)CrossRefGoogle Scholar
  10. 10.
    Zhang, T., Ge, S.S., Hang, C.C.: Adaptive Neural Network Control for Strict-Feedback Nonlinear Systems Using Backstepping Design. Automatica 12, 1835–1846 (2000)MathSciNetGoogle Scholar
  11. 11.
    Ge, S.S., Wang, C.: Adaptive NN Control of Uncertain Nonlinear Pure-Feedback Systems. Automatica 4, 671–682 (2002)CrossRefMathSciNetGoogle Scholar
  12. 12.
    Jin, Y.Q.: Nonlinear Adaptive Control System Design for Missile, Yantai, P.R.China (2003)Google Scholar
  13. 13.
    Han, J., Wang, W.: Nonlinear Tracking Differentiator. System Science and Mathematics 2, 177–183 (1994)Google Scholar
  14. 14.
    Ordonez, R., Spooner, J.T.: Stable Multi-input Multi-output Adaptive Fuzzy Control. In: Proceedings of the 35th CDC, Japan, pp. 610–615 (1996)Google Scholar
  15. 15.
    Khalil, H.K.: Adaptive Output Feedback Control of Nonlinear Systems Represented by Input-Output Models. IEEE Transactions on Automatic Control 2, 177–188 (1996)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Yunan Hu
    • 1
    • 2
  • Yuqiang Jin
    • 2
  • Pingyuan Cui
    • 1
  1. 1.Department of Astronautics and MechanicsHarbin Institute of TechnologyHarbinP.R.China
  2. 2.Department of Automatic ControlNaval Aeronautical Engineering AcademyYan taiP.R.China

Personalised recommendations