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Reconfigurable fault-tolerant attitude tracking for spacecraft with unknown nonlinear dynamics using neural network estimators with learning-type weight updating

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Abstract

This study investigated the problem of robust and reconfigurable attitude-tracking control with fault-tolerant capability for spacecraft under nonlinear inertia uncertainties, disturbance torques, and actuator faults. To improve the accuracy of reconstructing actuator faults, we proposed a nonlinear learning neural network estimator that combines the radial basis function neural network (RBFNN) model with an iterative learning algorithm, enabling the arbitrary precision of actuator fault reconstruction. A P-type iterative learning algorithm successively updates the RBFNN’s weight with a low computational load. Moreover, to ensure fast and robust spacecraft attitude fault-tolerant tracking, the learning RBFNN was integrated into a sliding mode control (SMC) scheme, leading to a learning neural-network SMC (LNNSMC), designed using the separation principle. The learning RBFNN was utilized to approximate and compensate for unknown nonlinear attitude dynamics online. Finally, the superiority of the presented method was demonstrated through a numerical example.

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Funding

This work was supported in part by the National Natural Science Foundation of China (Grant No. 61703276, 12172168), the National Key R & D Program of China (2022YFB3902801), the Fundamental Research Funds for the Central Universities (No. JUSRP123063), and the National Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Science and ICT) (No. NRF-2020R1A2C1005449).

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Authors and Affiliations

  1. Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China

    Qingxian Jia

  2. The Key Laboratory of Advanced Control for Light Industry Processes, Jiangnan University, Ministry of Education, 214122, Wuxi, China

    Chengxi Zhang

  3. The School of Electrical Engineering, Korea University, Seoul, 136-701, South Korea

    Choon Ki Ahn

  4. Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Kowloon, Hong Kong, SAR, China

    Jin Wu & Ming Liu

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  1. Qingxian Jia
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  2. Chengxi Zhang
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  3. Choon Ki Ahn
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  4. Jin Wu
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  5. Ming Liu
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Correspondence to Qingxian Jia or Choon Ki Ahn.

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Jia, Q., Zhang, C., Ahn, C.K. et al. Reconfigurable fault-tolerant attitude tracking for spacecraft with unknown nonlinear dynamics using neural network estimators with learning-type weight updating. Nonlinear Dyn 112, 8213–8227 (2024). https://doi.org/10.1007/s11071-024-09502-1

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