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Adaptive practical predefined-time neural tracking control for multi-joint uncertain robotic manipulators with input saturation

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Abstract

Predefined-time stability contributes to constraining the tracking time of robotic manipulators, but the stringent judgment conditions restrict its practical application. This paper studies an adaptive practical predefined-time neural control scheme for uncertain multi-joint robotic manipulators with input saturation. First, a practical predefined-time stability criterion is established to ensure the closed-loop stability of uncertain systems. Then, the unknown robotic dynamic model can be approximated by radial basis function neural networks. Meanwhile, the input saturation of the robotic manipulator is compensated by introducing an adaptive term. Based on the constructed stability criterion, the proposed controller is proved to guarantee that the tracking error of the system converges to a small neighborhood of the origin within a predefined time and is independent of the initial state. Finally, the effectiveness of the proposed control scheme is emphasized by numerical simulations and experiments of a two-joint and a nine-joint robotic manipulator, respectively.

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Acknowledgements

This work is partially supported by the National Natural Science Foundation of China (11972343).

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Authors and Affiliations

  1. CAS Key Laboratory of On-Orbit Manufacturing and Integration for Space Optics System, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, 130033, China

    Huayang Sai, Zhenbang Xu & Enyang Zhang

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Huayang Sai & Enyang Zhang

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  1. Huayang Sai
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  2. Zhenbang Xu
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Correspondence to Zhenbang Xu.

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Sai, H., Xu, Z. & Zhang, E. Adaptive practical predefined-time neural tracking control for multi-joint uncertain robotic manipulators with input saturation. Neural Comput & Applic 35, 20423–20440 (2023). https://doi.org/10.1007/s00521-023-08797-2

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