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Neural Processing Letters
Erschienen in: Neural Processing Letters 1/2020

29.05.2020

Adaptive Sampled-Data Observer Design for a Class of Nonlinear Systems with Unknown Hysteresis

verfasst von: Pengpeng Li, Yanjun Shen

Erschienen in: Neural Processing Letters | Ausgabe 1/2020

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Abstract

In this paper, a novel adaptive sampled-data observer design is studied for a class of nonlinear systems with unknown Prandtl–Ishlinskii hysteresis and unknown unmatched disturbances based on radial basis function neural networks (RBFNNs). To begin with, we investigate a sampled-data nonlinear system and present sufficient conditions such that the sampled-data nonlinear system is ultimately uniformly bounded (UUB). Then, an adaptive sampled-data observer is designed to estimate the unknown states of the nonlinear system. The unknown hysteresis and the unknown disturbances are approximated by RBFNNs. We also give the learning laws of the weights of RBFNNs, and prove that the estimation errors of the states and the weights are UUB, based on the obtained sufficient conditions and a special constructing Lyapunov–Krasovskii function. Finally, the effectiveness of the proposed design method is verified by numerical simulations.
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Metadaten
Titel
Adaptive Sampled-Data Observer Design for a Class of Nonlinear Systems with Unknown Hysteresis
verfasst von
Pengpeng Li
Yanjun Shen
Publikationsdatum
29.05.2020
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 1/2020
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-020-10275-y

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