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Adaptive Neural Fixed-time Sliding Mode Control of Uncertain Robotic Manipulators with Input Saturation and Prescribed Constraints

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Abstract

In this article, the issue of adaptive neural fixed-time tracking control for uncertain robotic manipulators subject to input saturation, external disturbance and prescribed constraints is studied. To handle the influence of input saturation, a novel auxiliary nonlinear dynamic system is constructed in which the system state is fixed-time stable. Radial basis function neural networks (RBF NNs) are used to approximate the system uncertainty. Instead of adjusting all weight vectors of RBF NNs, only one parameter is needed to be updated online. Then, based on performance function and auxiliary dynamic system, a fixed-time sliding mode controller with prescribed transient and steady-state performance is developed. Through theoretical analysis, it is concluded that the position tracking error can stabilize around the equilibrium point in fixed time and satisfy the prescribed requirements. Meanwhile, all signals in the closed-loop system are proved to be fixed-time stable by using the Lyapunov method. Finally, simulation results are presented to demonstrate the effectiveness of the proposed method.

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Acknowledgements

This work was supported by Science and Technology Planning Project of Guangdong Province (2015B010133002 and 2017B090910011).

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Authors and Affiliations

  1. School of Automation Science and Engineering, South China University of Technology, Guangzhou, Guangdong, 510640, China

    Yuxiang Wu, Haoran Fang, Tian Xu & Fuxi Wan

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  1. Yuxiang Wu
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  2. Haoran Fang
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  3. Tian Xu
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  4. Fuxi Wan
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Correspondence to Haoran Fang.

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Wu, Y., Fang, H., Xu, T. et al. Adaptive Neural Fixed-time Sliding Mode Control of Uncertain Robotic Manipulators with Input Saturation and Prescribed Constraints. Neural Process Lett 54, 3829–3849 (2022). https://doi.org/10.1007/s11063-022-10788-8

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  • DOI: https://doi.org/10.1007/s11063-022-10788-8

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