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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Ge, S., Wang, C.: Direct Adaptive NN Control of A Class of Nonlinear Systems. IEEE Trans. Neural Networks 13, 214–221 (2002)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chen, D., Yang, J. (2006). Implementable Adaptive Backstepping Neural Control of Uncertain Strict-Feedback Nonlinear Systems. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760023_129
Download citation
DOI: https://doi.org/10.1007/11760023_129
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34437-7
Online ISBN: 978-3-540-34438-4
eBook Packages: Computer ScienceComputer Science (R0)