Advertisement

Neural Adaptive Singularity-Free Control by Backstepping for Uncertain Nonlinear Systems

  • Zhandong Yu
  • Qingchao Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3498)

Abstract

An adaptive neural control approach based on backstepping is presented. Unmodelled dynamics or external disturbances are considered in the nonlinear models. Approximating nonlinear dynamic is one of the performances of multi-layer feedforward neural networks. By the Lyapunov’s stability theory, the NN weights are turned on-line with no prior training needed. An important feature of the presented approach is that by modifying a novel quasi-weighted Lyapunov function, the possible control singularity in the design of NN adaptive controller is avoided effectively. Finally, the approach is applied in CSTR system. The simulation result is given to demonstrate the feasibility and effectiveness of the proposed method.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Kanellakopoulos, L., Kokotovic, P.V., Morse, A.S.: Systematic Design of Adaptive Controllers for Feedback Linearizable Systems. IEEE Transactions on Automatic Control 36, 1241–1253 (1991)zbMATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    Krstic, M., Kanellakopoulos, I., Kokotovic, P.V.: Adaptive Nonlinear Control without Overparametrization. Systems & Control Letters 19, 177–185 (1992)zbMATHCrossRefMathSciNetGoogle Scholar
  3. 3.
    Polycarpou, M.M., Ioannou, P.A.: A Robust Adaptive Nonlinear Control Design. Automatica 32, 423–427 (1996)zbMATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Yao, B., Tomizuka, M.: Adaptive Robust Control of Nonlinear Systems in a Semi-Strict Feedback Form. Automatica 33, 893–900 (1997)zbMATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    Zhang, T., Ge, S.S., Hang, C.C.: Adaptive Neural Network Control for Strict-Feedback Nonlinear Systems Using Backstepping Design. Automatica 36, 1835–1846 (2000)zbMATHMathSciNetGoogle Scholar
  6. 6.
    Wang, D., Huang, J.: Adaptive Neural Network Control for a Class of Uncertain Nonlinear Systems in Pure-Feedback Form. Automatica 38, 1365–1372 (2002)zbMATHCrossRefGoogle Scholar
  7. 7.
    Hornik, K.: Approximation Capabilities of Multilayer Feed-Forward Networks. Neural Networks 4, 251–257 (1991)CrossRefGoogle Scholar
  8. 8.
    Kwan, C., Lewis, F.L., Dawson, D.M.: Robust Neural-Network Control of Rigid-Link Electrically Driven Robots. IEEE Transactions on Neural Networks 9, 581–588 (1998)CrossRefGoogle Scholar
  9. 9.
    Polycarpou, M.M.: Stable Adaptive Neural Control Scheme for Nonlinear Systems. IEEE Transactions on Automatic Control 41, 447–451 (1996)zbMATHCrossRefMathSciNetGoogle Scholar
  10. 10.
    Ge, S.S., Hang, C.C., Zhang, T.: A Direct Adaptive Controller for Dynamic Systems with a Class of Nonlinear Parameterizations. Automatica 35, 741–747 (1999)zbMATHCrossRefMathSciNetGoogle Scholar
  11. 11.
    Ge, S.S., Hang, C.C., Zhang, T.: Nonlinear Adaptive Control Using Neural Networks and its Application to CSTR Systems. Journal of Process Control 9, 313–323 (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Zhandong Yu
    • 1
  • Qingchao Wang
    • 1
  1. 1.Astronautics SchoolHarbin Institute of TechnologyHarbinChina

Personalised recommendations