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Volume 9 Issue 3
Mar.  2022

IEEE/CAA Journal of Automatica Sinica

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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

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

doi: 10.1109/JAS.2021.1004216
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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  • 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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  • [1]
    G. Curzi, D. Modenini, and P. Tortora, “Large constellations of small satellites: A survey of near future challenges and missions,” Aerospace, vol. 7, no. 9, p. 133, 2020.
    [2]
    K. Shi, C. Liu, J. D. Biggs, Z. Sun, and X. Yue, “Observer-based control for spacecraft electromagnetic docking,” Aerospace Science and Technology, vol. 99, p. 105759, 2020.
    [3]
    W. Clohessy and R. Wiltshire, “Terminal guidance system for satellite rendezvous,” Journal of the Aerospace Sciences, vol. 27, no. 9, pp. 653–658, 1960. doi: 10.2514/8.8704
    [4]
    D. P. Scharf, F. Y. Hadaegh, and S. R. Ploen, “A survey of spacecraft formation flying guidance and control. Part II: Control,” in Proc. IEEE American Control Conf., 2004, pp. 2976–2985.
    [5]
    H. Wong, V. Kapila, and A. G. Sparks, “Adaptive output feedback tracking control of spacecraft formation,” Int. Journal of Robust and Nonlinear Control, vol. 12, no. 2–3, pp. 117–139, 2002. doi: 10.1002/rnc.679
    [6]
    J. Slotine and W. Li, Applied Nonlinear Control. Prentice Hall Englewood Cliffs, NJ, 1991.
    [7]
    N. Vafamand, “Adaptive robust neural-network-based backstepping control of tethered satellites with additive stochastic noise,” IEEE Trans. Aerospace and Electronic Systems, vol. 56, no. 5, pp. 3922–3930, 2020. doi: 10.1109/TAES.2020.2985276
    [8]
    R. Haghighi and C. K. Pang, “Robust concurrent attitude-position control of a swarm of underactuated nanosatellites,” IEEE Trans. Control Systems Technology, vol. 26, no. 1, pp. 77–88, 2018. doi: 10.1109/TCST.2017.2656025
    [9]
    M. D. Queiroz, V. Kapila, and Q. Yan, “Adaptive nonlinear control of multiple spacecraft formation flying,” Journal of Guidance Control and Dynamics, vol. 23, pp. 385–390, 2000. doi: 10.2514/2.4549
    [10]
    L. Sun and Z. Zheng, “Adaptive relative pose control of spacecraft with model couplings and uncertainties,” Acta Astronautica, vol. 143, pp. 29–36, 2018. doi: 10.1016/j.actaastro.2017.11.006
    [11]
    Weiping, J. J. Li, E., and Slotine, “Indirect adaptive robot control,” in Proc. IEEE Int. Conf. Robotics & Automation, vol. 2, 1988, pp. 704–709.
    [12]
    W. Bai, T. Li, and S. Tong, “NN reinforcement learning adaptive control for a class of nonstrict-feedback discrete-time systems,” IEEE Trans. Cybernetics, vol. 50, no. 11, pp. 4573–4584, 2020.
    [13]
    J. Huang, W. Wang, C. Wen, and J. Zhou, “Adaptive control of a class of strict-feedback time-varying nonlinear systems with unknown control coefficients,” Automatica, vol. 93, pp. 98–105, 2018. doi: 10.1016/j.automatica.2018.03.061
    [14]
    P. A. Ioannou and P. V. Kokotovic, “Instability analysis and improvement of robustness of adaptive control,” Automatica, vol. 20, no. 5, pp. 583–594, 1984. doi: 10.1016/0005-1098(84)90009-8
    [15]
    S. Yin, H. Yang, H. Gao, J. Qiu, and O. Kaynak, “An adaptive NN-based approach for fault-tolerant control of nonlinear time-varying delay systems with unmodeled dynamics,” IEEE Trans. Neural Networks and Learning Systems, vol. 28, no. 8, pp. 1902–1913, 2016.
    [16]
    H. Sun, H. Zhao, K. Huang, S. Zhen, and Y.-H. Chen, “Adaptive robust constraint-following control for satellite formation flying with system uncertainty,” Journal of Guidance,Control,and Dynamics, vol. 40, no. 6, pp. 1492–1502, 2017. doi: 10.2514/1.G002396
    [17]
    A. Astolfi and R. Ortega, “Immersion and invariance: A new tool for stabilization and adaptive control of nonlinear systems,” IEEE Trans. Automatic Control, vol. 48, no. 4, pp. 590–606, 2003. doi: 10.1109/TAC.2003.809820
    [18]
    A. Astolfi, D. Karagiannis, and R. Ortega, “Stabilization of uncertain nonlinear systems via immersion and invariance,” Eur. J. Control, vol. 13, pp. 204–220, 2007. doi: 10.3166/ejc.13.204-220
    [19]
    D. Seo and M. R. Akella, “High-performance spacecraft adaptive attitude-tracking control through attracting-manifold design,” Journal of Guidance Control &Dynamics, vol. 31, no. 4, pp. 884–891, 2008.
    [20]
    K. W. Lee and S. Singh, “Noncertainty-equivalence spacecraft adaptive formation control with filtered signals,” Journal of Aerospace Engineering, vol. 30, p. 04017029, 2017.
    [21]
    P. Krishnamurthy and F. Khorrami, “Dynamic high-gain scaling: State and output feedback with application to systems with iss appended dynamics driven by all states,” IEEE Trans. Automatic Control, vol. 49, no. 12, pp. 2219–2239, 2004. doi: 10.1109/TAC.2004.839235
    [22]
    D. Karagiannis, M. Sassano, and A. Astolfi, “Dynamic scaling and observer design with application to adaptive control,” Automatica, vol. 45, no. 12, pp. 2883–2889, 2009. doi: 10.1016/j.automatica.2009.09.013
    [23]
    H. Wen, X. Yue, L. Peng, and J. Yuan, “Fast spacecraft adaptive attitude tracking control through immersion and invariance design,” Acta Astronautica, vol. 139, pp. 77–84, 2017. doi: 10.1016/j.actaastro.2017.06.024
    [24]
    K. W. Lee and S. Singh, “Noncertainty-equivalence adaptive attitude control of satellite orbiting around an asteroid,” Acta Astronautica, vol. 161, pp. 24–39, 2019. doi: 10.1016/j.actaastro.2019.05.008
    [25]
    K. P. Tee, S. S. Ge, and E. H. Tay, “Barrier Lyapunov functions for the control of output-constrained nonlinear systems,” Automatica, vol. 45, no. 4, pp. 918–927, 2009. doi: 10.1016/j.automatica.2008.11.017
    [26]
    B. Ren, S. S. Ge, K. P. Tee, and T. H. Lee, “Adaptive neural control for output feedback nonlinear systems using a barrier Lyapunov function,” IEEE Trans. Neural Networks, vol. 21, no. 8, pp. 1339–1345, 2010. doi: 10.1109/TNN.2010.2047115
    [27]
    D. Li, C. Chen, Y. Liu, and S. Tong, “Neural network controller design for a class of nonlinear delayed systems with time-varying fullstate constraints,” IEEE Trans. Neural Networks and Learning Systems, vol. 30, no. 9, pp. 2625–2636, 2019. doi: 10.1109/TNNLS.2018.2886023
    [28]
    L. Sun, W. Huo, and Z. Jiao, “Adaptive backstepping control of spacecraft rendezvous and proximity operations with input saturation and full-state constraint,” IEEE Trans. Industrial Electronics, vol. 64, no. 1, pp. 480–492, 2017. doi: 10.1109/TIE.2016.2609399
    [29]
    L. Sun, “Saturated adaptive output-constrained control of cooperative spacecraft rendezvous and docking,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 6, pp. 1462–1470, 2019.
    [30]
    C. Liu, G. Vukovich, Z. Sun, and K. Shi, “Observer-based fault-tolerant attitude control for spacecraft with input delay,” Journal of Guidance Control and Dynamics, vol. 41, pp. 2041–2053, 2018. doi: 10.2514/1.G003555
    [31]
    A. Bounemeur, M. Chemachema, and N. Essounbouli, “Indirect adaptive fuzzy fault-tolerant tracking control for MIMO nonlinear systems with actuator and sensor failures,” ISA Transactions, vol. 79, pp. 45–61, 2018. doi: 10.1016/j.isatra.2018.04.014
    [32]
    A. Bounemeur, M. Chemachema, A. Zahaf, and S. Bououden, “Adaptive fuzzy fault-tolerant control using nussbaum gain for a class of SISO nonlinear systems with unknown directions,” in Proc. 4th Int. Conf. Electrical Engineering and Control Applications, 2019.
    [33]
    H. Yang and H. Wang, “Robust adaptive fault-tolerant control for uncertain nonlinear system with unmodeled dynamics based on fuzzy approximation,” Neurocomputing, vol. 173, pp. 1660–1670, 2016. doi: 10.1016/j.neucom.2015.09.039
    [34]
    Q. Hu, Y. Shi, and X. Shao, “Adaptive fault-tolerant attitude control for satellite reorientation under input saturation,” Aerospace Science and Technology, vol. 78, pp. 171–182, 2018. doi: 10.1016/j.ast.2018.04.015
    [35]
    Y. Ou, H. Zhang, and B. Li, “Absolute orbit determination using line-of-sight vector measurements between formation flying spacecraft,” Astrophysics and Space Science, vol. 363, no. 4, pp. 1–13, 2018.
    [36]
    T. Chen and S. Xu, “Double line-of-sight measuring relative navigation for spacecraft autonomous rendezvous,” Acta Astronautica, vol. 67, no. 1–2, pp. 122–134, 2010. doi: 10.1016/j.actaastro.2009.12.010
    [37]
    K. Shi, C. Liu, and Z. Sun, “Constrained fuel-free control for spacecraft electromagnetic docking in elliptical orbits,” Acta Astronautica, vol. 162, pp. 14–24, 2019. doi: 10.1016/j.actaastro.2019.05.016
    [38]
    J. Nestruev, A. Bocharov, and S. Duzhin, Smooth Manifolds and Observables. Springer, 2003.

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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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