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
Hauptverfasser: Chen, Bing, Zhang, Huaguang, Lin, Chong
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.
ISSN:2162-237X
2162-2388
DOI:10.1109/TNNLS.2015.2412121