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Adaptive Neural Network Control of Uncertain Systems with Full State Constraints and Unknown Gain Sign

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Proceedings of 2019 Chinese Intelligent Systems Conference (CISC 2019)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 592))

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Abstract

In this paper, adaptive neural network control is proposed based on improved dynamic surface control (DSC) method and the approximation capability of radial basis function (RBF) neural networks (NNs) for a class of uncertain constrained pure-feedback nonlinear systems with unmodeled dynamics and unknown gain sign. By constructing a one to one nonlinear mapping, the pure-feedback system with full state constraints is transformed into a novel pure-feedback system without state constraints. The dynamic uncertainties are handled using an auxiliary dynamic signal. Using mean value theorem and Nussbaum function, an adaptive NN control scheme is developed based on the transformed system. The designed control strategy removes the conditions that the upper bound of the control gain is known, and the lower bounds and upper bounds of the virtual control coefficients are known. By theoretical analysis, all the signals in the closed-loop system are shown to be semi-globally uniformly ultimately bounded (SGUUB), and the full state constraints are not violated. A numerical example is provided to demonstrate the effectiveness of the proposed method.

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Acknowledgments

This work was partially supported by the National Natural Science Foundation of China (61573307), the Natural Science Foundation of Jiangsu Province (BK20181218) 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

    Heqing Liu, Tianping Zhang, Meizhen Xia & Ziwen Wu

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

Correspondence to Tianping Zhang .

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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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Liu, H., Zhang, T., Xia, M., Wu, Z. (2020). Adaptive Neural Network Control of Uncertain Systems with Full State Constraints and Unknown Gain Sign. In: Jia, Y., Du, J., Zhang, W. (eds) Proceedings of 2019 Chinese Intelligent Systems Conference. CISC 2019. Lecture Notes in Electrical Engineering, vol 592. Springer, Singapore. https://doi.org/10.1007/978-981-32-9682-4_54

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