Adaptive Prescribed-Time Agile Control for Hypersonic Morphing Vehicles
Keywords:
Hypersonic Morphing Vehicles, Prescribed-time Control, Adaptive Dynamic Programming, Sliding Mode ControlAbstract
For the agile control of hypersonic morphing vehicles during high-maneuverability flight, an adaptive prescribed-time agile control method is proposed. To address model mismatch and agile control challenges arising from unsteady aerodynamic characteristics during high-maneuver hypersonic variable-shape vehicles, an adaptive prescribed-time agile control method is proposed. This approach employs prescribed-time sliding mode control as the baseline controller, integrated with adaptive dynamic programming to reduce the theoretical upper bound of prescribed time while enhancing agility. Furthermore, through non-singular control input design, the method maintains the specified time-constrained implementation while preserving the predetermined ratio between system bandwidth and actuator bandwidth. Based on Lyapunov theory, the stability of the closed-loop controller was demonstrated. Finally, a set of comparative simulations demonstrates that the proposed control method achieves prescribed-time convergence while exhibiting lower conservatism, stronger model adaptability, and higher control accuracy. Additionally, the controller possesses input optimization capabilities that significantly reduce the torque required for computation.
References
[1] Wuyu P, Tao Y, et al. Analysis on wing deformation modes of hypersonic morphing aircraft [J]. Journal of National University of Defense Technology 2018; 40(3): 15-21.
[2] Gui C, Guang Y, Hongwei G, et al. Review on key technologies for hypersonic morphing aircraft [J]. Aeronautical Science & Technology 2024; 35(05): 28-44.
[3] Chu L, Qi L, Feng G, et al. Design, modeling, and control of morphing aircraft: A review[J]. Chinese journal of aeronautics 2022; 35(5): 220-246.
https://doi.org/10.1016/j.cja.2021.09.013
[4] Mengdan C, Yong W, Yanan G. Roll Control and Rudder System’s Technologic Parameters of Hypersonic Vehicle in Reentry Phase[J]. Space Control and Application 2016; 42(4): 18-23.
[5] Boyi C, Yanbin L, Hao L, et al. Stability boundary analysis of hypersonic vehicle with control saturation and bandwidth limitation [J]. Control Theory & Applications 2016; 33(11): 1508-1518.
[6] Wang L, Zhang W, Peng K, et al. Adaptive Command Filtered Integrated Guidance and Control for Hypersonic Vehicle with Magnitude, Rate and Bandwidth Constraints[C], MATEC Web of Conferences. EDP Sciences 2018; 151: 05004.
https://doi.org/10.1051/matecconf/201815105004
[7] He Z, Yin M, Lu Y. Tensor product model-based control of morphing aircraft in transition process[J]. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 2016; 230(2): 378-391.
https://doi.org/10.1177/0954410015591835
[8] Yue T, Wang L, Ai J. Gain self-scheduled H∞ control for morphing aircraft in the wing transition process based on an LPV model[J]. Chinese Journal of Aeronautics 2013; 26(4): 909-917.
https://doi.org/10.1016/j.cja.2013.06.004
[9] Qing W, Huichuan Y, Chaoyang D. Switching LPV control of morphing aircraft based on overlapped parameter area[J]. Journal of Shenyang University of Technology 2013; 35(06): 698-703.
[10] Wu Q, Liu Z, Liu F, et al. LPV-based self-adaption integral sliding mode controller with L2 gain performance for a morphing aircraft[J]. IEEE Access 2019; 7: 81515-81531.
https://doi.org/10.1109/ACCESS.2019.2923313
[11] Pu J, Zhang Y, Guan Y, et al. Recurrent neural network-based predefined time control for morphing aircraft with asymmetric time-varying constraints[J]. Applied Mathematical Modelling 2024; 135: 578-600.
https://doi.org/10.1016/j.apm.2024.06.024
[12] Changzhu W, Xin G, Yulong L. Fixed-time Anti-saturation Control for Hypersonic Morphing Flight Vehicle [J]. Journal of Astronautics 2025; 46(4): 731-740
[13] Yuan Z, Wanwei H, Kunfeng L, et al. Modeling and finite-time control of hypersonic morphing aircraft [J]. Journal of Beijing University of Aeronautics and Astronautics 2022; 48(10): 1979-1993.
[14] Hao Z, Peng W, Guojian T, et al. Event-triggered finite-time control for hypersonic morphing aircraft [J]. Acta Aeronautica et Astronautica Sinica 2023; 44(15): 325-338.
[15] Chengyu C, Fanbiao L, Yuxin L, et al. Modeling and fixed-time prescribed-performance control of hypersonic morphing aircraft [J]. Acta Automatica Sinica 2024; 50(3): 486-504.
[16] Jiao X, Fidan B, Jiang J, et al. Adaptive mode switching of hypersonic morphing aircraft based on type-2 TSK fuzzy sliding mode control[J]. Science China Information Sciences 2015; 58(7): 1-15.
https://doi.org/10.1007/s11432-015-5349-z
[17] Liu H, Zhang Q, Cui L, et al. Attitude control of hypersonic morphing aircraft based on incremental backsteping sliding mode[C] 2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP). IEEE 2022; 1324-1331.
https://doi.org/10.1109/ICSP54964.2022.9778601
[18] Zhang H, Bao C, Ma W, et al. Prescribed-time Attitude Control for Hypersonic Morphing Vehicles Using Morphing Information-Driven Events[J]. IEEE Transactions on Aerospace and Electronic Systems 2024.
https://doi.org/10.1109/TAES.2024.3404911
[19] Bao C, Wang P, Tang G. Data-driven based model-free adaptive optimal control method for hypersonic morphing vehicle[J]. IEEE Transactions on Aerospace and Electronic Systems 2022; 59(4): 3713-3725.
https://doi.org/10.1109/TAES.2022.3230633
[20] Li S, Shao X, Wang H, et al. Adaptive Critic Attitude Learning Control for Hypersonic Morphing Vehicles without Backsteping[J]. IEEE Transactions on Aerospace and Electronic Systems 2025.
https://doi.org/10.1109/TAES.2025.3542345
[21] Zhao S, Wang J, Xu H, et al. ADP-based attitude-tracking control with prescribed performance for hypersonic vehicles[J]. IEEE Transactions on Aerospace and Electronic Systems 2023; 59(5): 6419-6431.
https://doi.org/10.1109/TAES.2023.3276729
[22] Yuan Y, Wang Z, Guo L, et al. Barrier Lyapunov functions-based adaptive fault tolerant control for flexible hypersonic flight vehicles with full state constraints[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems 2018; 50(9): 3391-3400.
https://doi.org/10.1109/TSMC.2018.2837378
[23] Modares H, Sistani MBN, Lewis FL. A policy iteration approach to online optimal control of continuous-time constrained-input systems[J]. ISA transactions 2013; 52(5): 611-621.
https://doi.org/10.1016/j.isatra.2013.04.004
[24] Vamvoudakis KG, Lewis FL. Online actor–critic algorithm to solve the continuous-time infinite horizon optimal control problem. Automatica 2010; 46: 878-888.
https://doi.org/10.1016/j.automatica.2010.02.018
[25] Abu-Khalaf M, Lewis FL. Nearly optimal control laws for nonlinear systems with saturating actuators using a neural network HJB approach. Automatica 2005; 41(5): 779-791.
https://doi.org/10.1016/j.automatica.2004.11.034
[26] Wang N, Ying G, Xuefeng Z. Data-driven performance-prescribed reinforcement learning control of an unmanned surface vehicle. IEEE Transactions on Neural Networks and Learning Systems 2021; 32(12): 5456-5467.
