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Adaptive Neural Control of Stochastic Nonlinear System with Dynamic Uncertainties

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Proceedings of 2018 Chinese Intelligent Systems Conference

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 529))

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Abstract

In this paper, an adaptive neural dynamic surface control (DSC) scheme is developed for a class of stochastic nonlinear systems in the presence of input and state unmodeled dynamics. A dynamic signal is employed to handle the state unmodeled dynamics. A normalization signal and a novel adjustable parameter are used to deal with the input unmodeled dynamics. By theoretical analysis, it is shown that all the signals in the closed-loop system are bounded in probability.

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Acknowledgements

This work was partially supported by the National Natural Science Foundation of China (61573307 and 61473250) and Yangzhou University Top-level Talents Support Program (2016).

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Authors and Affiliations

  1. College of Information Engineering, Yangzhou University, Yangzhou, 225127, China

    Meizhen Xia, Tianping Zhang & Ziwen Wu

Authors
  1. Meizhen Xia
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  2. Tianping Zhang
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  3. Ziwen Wu
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Corresponding author

Correspondence to Tianping Zhang .

Editor information

Editors and Affiliations

  1. Beihang University, Beijing, China

    Yingmin Jia

  2. Beijing University of Posts and Telecommunications, Beijing, China

    Junping Du

  3. University of Science and Technology Beijing, Beijing, China

    Weicun Zhang

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Xia, M., Zhang, T., Wu, Z. (2019). Adaptive Neural Control of Stochastic Nonlinear System with Dynamic Uncertainties. In: Jia, Y., Du, J., Zhang, W. (eds) Proceedings of 2018 Chinese Intelligent Systems Conference. Lecture Notes in Electrical Engineering, vol 529. Springer, Singapore. https://doi.org/10.1007/978-981-13-2291-4_18

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