Please use this identifier to cite or link to this item: https://doi.org/10.1109/TNN.2004.839354
Title: Neural network-based adaptive dynamic surface control for a class of uncertain nonlinear systems in strict-feedback form
Authors: Wang, D. 
Huang, J.
Keywords: Adaptive control
Neural networks
Nonlinear control
Strict-feedback systems
Issue Date: Jan-2005
Citation: Wang, D., Huang, J. (2005-01). Neural network-based adaptive dynamic surface control for a class of uncertain nonlinear systems in strict-feedback form. IEEE Transactions on Neural Networks 16 (1) : 195-202. ScholarBank@NUS Repository. https://doi.org/10.1109/TNN.2004.839354
Abstract: The dynamic surface control (DSC) technique was developed recently by Swaroop et al. This technique simplified the backstepping design for the control of nonlinear systems in strict-feedback form by overcoming the problem of "explosion of complexity" It was later extended to adaptive backstepping design for nonlinear systems with linearly parameterized uncertainty. In this paper, by incorporating this design technique into a neural network based adaptive control design framework, we have developed a backstepping based control design for a class of nonlinear systems in strict-feedback form with arbitrary uncertainty. Our development is able to eliminate the problem of "explosion of complexity" inherent in the existing method. In addition, a stability analysis is given which shows that our control law can guarantee the uniformly ultimate boundedness of the solution of the closed-loop system, and make the tracking error arbitrarily small. © 2005 IEEE.
Source Title: IEEE Transactions on Neural Networks
URI: http://scholarbank.nus.edu.sg/handle/10635/111444
ISSN: 10459227
DOI: 10.1109/TNN.2004.839354
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