Observer-Based Adaptive Neural Network Control for Nonlinear Systems in Nonstrict-Feedback Form
This paper focuses on the problem of adaptive neural network (NN) control for a class of nonlinear nonstrict-feedback systems via output feedback. A novel adaptive NN backstepping output-feedback control approach is first proposed for nonlinear nonstrict-feedback systems. The monotonicity of system...
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Veröffentlicht in: | IEEE transaction on neural networks and learning systems 2016-01, Vol.27 (1), p.89-98 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | This paper focuses on the problem of adaptive neural network (NN) control for a class of nonlinear nonstrict-feedback systems via output feedback. A novel adaptive NN backstepping output-feedback control approach is first proposed for nonlinear nonstrict-feedback systems. The monotonicity of system bounding functions and the structure character of radial basis function (RBF) NNs are used to overcome the difficulties that arise from nonstrict-feedback structure. A state observer is constructed to estimate the immeasurable state variables. By combining adaptive backstepping technique with approximation capability of radial basis function NNs, an output-feedback adaptive NN controller is designed through backstepping approach. It is shown that the proposed controller guarantees semiglobal boundedness of all the signals in the closed-loop systems. Two examples are used to illustrate the effectiveness of the proposed approach. |
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ISSN: | 2162-237X 2162-2388 |
DOI: | 10.1109/TNNLS.2015.2412121 |