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Volume 9 Issue 3
Mar.  2022

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2022  >  9(3): 482-495
Z. W. Hao, X. K. Yue, H. W. Wen, and C. Liu, “Full-state-constrained non-certainty-equivalent adaptive control for satellite swarm subject to input fault,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 3, pp. 482–495, Mar. 2022. doi: 10.1109/JAS.2021.1004216
Citation: Z. W. Hao, X. K. Yue, H. W. Wen, and C. Liu, “Full-state-constrained non-certainty-equivalent adaptive control for satellite swarm subject to input fault,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 3, pp. 482–495, Mar. 2022. doi: 10.1109/JAS.2021.1004216
shu

Full-State-Constrained Non-Certainty-Equivalent Adaptive Control for Satellite Swarm Subject to Input Fault

doi: 10.1109/JAS.2021.1004216

  • National Key Laboratory of Aerospace Flight Dynamics, School of Astronautics, Northwestern Polytechnical University, Xi’an 710072, China

Funds:  This work was supported by the Natural Science Foundation of Shaanxi Province (2020JQ-132), China Postdoctoral Science Foundation (2020M683571), National Natural Science Foundation of China (62103336, 11972026, U2013206), and the Fundamental Research Funds for the Central Universities (3102019HTQD007)
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    Abstract
  • Satellite swarm coordinated flight (SSCF) technology has promising applications, but its complex nature poses significant challenges for control implementation. In response, this paper proposes an easily solvable adaptive control scheme to achieve high-performance trajectory tracking of the SSCF system subject to actuator efficiency losses and external disturbances. Most existing adaptive controllers based on the certainty-equivalent (CE) principle show unpredictability and non-convergence in their online parameter estimations. To overcome the above vulnerabilities and the difficulties caused by input failures of SSCF, this paper proposes an adaptive estimator based on scaling immersion and invariance (I&I), which reduces the computational complexity while improving the performance of the parameter estimator. Besides, a barrier Lyapunov function (BLF) is applied to satisfy both the boundedness of the system states and the singularity avoidance of the computation. It is proved that the estimator error becomes sufficiently small to converge to a specified attractive invariant manifold and the closed-loop SSCF system can obtain asymptotic stability under full-state constraints. Finally, numerical simulations are performed for comparison and analysis to verify the effectiveness and superiority of the proposed method.

     

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    Highlights

    • Develop a BLF-DSA controller to stabilize the uncertain satellite swarm system
    • Provide a non-CE adaptive scheme to overcome system uncertainties and input fault
    • Guarantee predefined full-state constraints for satellite swarm with input fault
    • Introduce a dynamic scaling factor to reduce solving difficulty of the estimator

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