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Implementable Adaptive Backstepping Neural Control of Uncertain Strict-Feedback Nonlinear Systems

  • Dingguo Chen
  • Jiaben Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)

Abstract

Presented in this paper is neural network based adaptive control for a class of affine nonlinear systems in the strict-feedback form with unknown nonlinearities. A popular recursive design methodology – backstepping is employed to systematically construct feedback control laws and associated Lyapunov functions. The significance of this paper is to make best use of available signals, avoid unnecessary parameterization, and minimize the node number of neural networks as on-line approximators. The design assures that all the signals in the closed loop are semi-globally uniformly, ultimately bounded and the outputs of the system converges to a tunable small neighborhood of the desired trajectory. Novel parameter tuning algorithms are obtained on a more practical basis.

Keywords

Virtual Control Neural Controller Adaptive Neural Control Adaptive Control Design Control Design Scheme 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Ge, S., Wang, C.: Direct Adaptive NN Control of A Class of Nonlinear Systems. IEEE Trans. Neural Networks 13, 214–221 (2002)CrossRefGoogle Scholar
  2. 2.
    Li, Y., Qiang, S., Zhuang, X., Kaynak, O.: Robust and Adaptive Backstepping Control for Nonlinear Systems Using RBF Neural Networks. IEEE Trans. Neural Networks 15, 693–701 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Dingguo Chen
    • 1
  • Jiaben Yang
    • 2
  1. 1.Siemens Power Transmission and Distribution Inc.MinnetonkaUSA
  2. 2.Department of AutomationTsinghua UniversityBeijingPeople’s Republic of China

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