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Neural network-based robust finite-time attitude stabilization for rigid spacecraft under angular velocity constraint

  • Special Issue on Computational Intelligence-based Control and Estimation in Mechatronic Systems
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Abstract

In this paper, the problem of attitude stabilization control of spacecraft under angular velocity constraint is investigated. A state-constrained finite-time attitude control scheme is designed by making full use of the model feature of the quaternion. Based on the homogeneous domination approach, the finite-time stability of the closed-loop system is proved. It proves that the angular velocity can be constrained within the limited range at any time. For the attitude loop dynamics subsystem with external disturbances, based on a radial basis function neural network, an integral terminal sliding mode controller is proposed. Finally, the validity and advantages of the proposed control scheme are demonstrated in the simulation section compared with the other two control methods.

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Authors and Affiliations

  1. School of Electrical Engineering and Automation, Hefei University of Technology, Hefei, 230009, People’s Republic of China

    Bo Yu, Haibo Du, Lijian Ding & Hua Li

  2. Engineering Technology search Center of Industrial Automation, Hefei, 230009, Anhui Province, People’s Republic of China

    Haibo Du

  3. School of Automation, Southeast University, Nanjing, 210096, People’s Republic of China

    Di Wu

Authors
  1. Bo Yu
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  2. Haibo Du
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  3. Lijian Ding
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  4. Di Wu
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  5. Hua Li
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Correspondence to Haibo Du.

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This work is supported by National Natural Science Foundation of China under Grant Nos. 62073113, 62003122, 61673153, 61773216, Natural Science Foundation of Anhui Province of China under Grant Nos. 2008085UD03, 1808085MF180, and the Fundamental Research Funds for the Central Universities of China under Grant No. PA2020GDKC0016.

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Yu, B., Du, H., Ding, L. et al. Neural network-based robust finite-time attitude stabilization for rigid spacecraft under angular velocity constraint. Neural Comput & Applic 34, 5107–5117 (2022). https://doi.org/10.1007/s00521-021-06056-w

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  • DOI: https://doi.org/10.1007/s00521-021-06056-w

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