To complete complex tasks with space robots in the impedance environment, sliding mode control (SMC) has been proposed and plays an important role in the past decades. Learning-based control has also met with great success in robotics and has been closely followed in the control of space robots. However, as an interdisciplinary, only a few studies have combined deep learning and traditional controller in the space robot directly. This paper proposes a novel fractional-order fixed-time SMC for impedance control of space robots, taking advantage of a model-based deep learning approach to obtain the dynamics of space robots. The learning results are used to make predictions for the data-based robot dynamics. The fractional-order fixed-time SMC controller is added to that data-based model, and it ensures the finite closed-loop convergence time. Small-dataset training is more reasonable in the situation of space robots, which reduces training time and sampling size. Compared to traditional methods, combining deep learning and fractional-order fixed-time SMC provides better tracking performance and improves closed-loop robustness. It accomplishes the control target in the impedance model using the estimated data-driven parameters. Theoretical analysis proves the closed-loop behavior, and simulation results verify its effectiveness.


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    Title :

    Dynamics-based deep learning and fractional-order fixed-time sliding mode control for space robot impedance system


    Contributors:
    Zhao, Tongyu (author) / Sun, Guanghui (author) / Zhu, Weihong (author)


    Publication date :

    2023-09-01


    Size :

    9 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





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