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Adaptive Neural Control for Output Constrained Block-Structure Affine Nonlinear Systems via Command Filter

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Proceedings of 2022 Chinese Intelligent Systems Conference (CISC 2022)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 950))

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Abstract

In this paper, adaptive neural tracking control is proposed by the aid of the error compensation mechanism for block-structure affine nonlinear systems with output restrictions and unmodeled dynamics. Based on the property of unmodeled dynamics, an available signal constructed by the first-order linear system is utilized to deal with the dynamical uncertainties. The unknown nonlinear smooth function produced in the design process of virtual control is estimated by radial basis function neural networks (RBFNNs). A barrier Lyapunov function is used to deal with the output restrictions. All the signals in the controlled system are shown to be semi-globally uniformly ultimately bounded (SGUUB) by introducing all compensation signals to the overall Lyapunov function and using the compact set in traditional dynamic surface control (DSC) in the stability analysis. A 2-link flexible joint robot system is selected to verify the feasibility of the developed control method.

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Acknowledgements

This work was partially supported by the National Natural Science Foundation of China (62073283).

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Authors and Affiliations

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

    Miao Shi & Tianping Zhang

  2. School of Information Science and Technology, Yancheng Teachers University, Yancheng, 224002, China

    Jianjiang Yu

  3. Yangzhou Weibang Garden Machine Company Limited, Yangzhou, 225127, China

    Baicheng Zhu

Authors
  1. Miao Shi
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  2. Jianjiang Yu
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  3. Tianping Zhang
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  4. Baicheng Zhu
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Corresponding author

Correspondence to Tianping Zhang .

Editor information

Editors and Affiliations

  1. School of Automation Science and Electrical Engineering, Beihang University, Beijing, China

    Yingmin Jia

  2. School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China

    Weicun Zhang

  3. School of Mechanical Engineering and Automation, Beihang University, Beijing, China

    Yongling Fu

  4. Beijing Institute of Precision Mechatronics and Controls, Beijing, China

    Shoujun Zhao

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Shi, M., Yu, J., Zhang, T., Zhu, B. (2022). Adaptive Neural Control for Output Constrained Block-Structure Affine Nonlinear Systems via Command Filter. In: Jia, Y., Zhang, W., Fu, Y., Zhao, S. (eds) Proceedings of 2022 Chinese Intelligent Systems Conference. CISC 2022. Lecture Notes in Electrical Engineering, vol 950. Springer, Singapore. https://doi.org/10.1007/978-981-19-6203-5_33

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  • DOI: https://doi.org/10.1007/978-981-19-6203-5_33

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-6202-8

  • Online ISBN: 978-981-19-6203-5

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