先进飞行器安全控制技术专刊

基于强迫振荡的战斗机有限时间深失速改出控制

  • 李钊星 ,
  • 杨林 ,
  • 王霞 ,
  • 许斌
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  • 1.西北工业大学 自动化学院,西安 710072
    2.航空工业 成都飞机设计研究所,成都 610041
    3.山东大学 控制科学与工程学院,济南 250061

收稿日期: 2025-06-28

  修回日期: 2025-07-21

  录用日期: 2025-12-15

  网络出版日期: 2025-12-23

基金资助

深圳市科技计划(JCYJ20230807145500002);国家自然科学基金(62403283);中国博士后科学基金(2025T180481);西北工业大学博士论文创新基金(CX2024071)

Finite-time deep stall recovery control for fighter aircraft using forced oscillation

  • Zhaoxing LI ,
  • Lin YANG ,
  • Xia WANG ,
  • Bin XU
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  • 1.School of Automation,Northwestern Polytechnical University,Xi’an 710072,China
    2.AVIC Chendu Aircraft Design Research Institute,Chendu 610041,China
    3.School of Control Science and Engineering,Shandong University,Jinan 250061,China

Received date: 2025-06-28

  Revised date: 2025-07-21

  Accepted date: 2025-12-15

  Online published: 2025-12-23

Supported by

Science, Technology and Innovation Commission of Shenzhen Municipality(JCYJ20230807145500002);National Natural Science Foundation of China(62403283);China Postdoctoral Science Foundation(2025T180481);Innovation Foundation of the University for Graduate Students in Northwestern Polytechnical University(CX2024071)

摘要

考虑战斗机深失速状态的快速稳定改出问题,提出一种基于强迫振荡的有限时间深失速改出控制方法。针对深失速状态动力学特性表征需求,采用分岔理论进行分析并通过反向时间积分求解深失速吸引域;为从机理上给出准确的深失速恢复指令,基于扩展分岔分析在控制器设计中引入不稳定分岔对应的强迫振荡偏摆响应;针对深失速状态下的时变扰动和气动参数摄动影响,设计扰动观测器估计干扰项并借助神经网络处理模型不确定性,结合迎角跟踪误差反馈得到深失速改出控制器;基于李雅普诺夫稳定性分析证明动力学与控制器结合的整体系统在有限时间内的收敛情况。结果表明:深失速中的战斗机能够降低迎角至安全区域并保持稳定可控,实现快速平稳的深失速状态恢复。

本文引用格式

李钊星 , 杨林 , 王霞 , 许斌 . 基于强迫振荡的战斗机有限时间深失速改出控制[J]. 航空学报, 2026 , 47(9) : 532491 -532491 . DOI: 10.7527/S1000-6893.2025.32491

Abstract

To address the problem of rapid and stable recovery from deep stall in fighter aircraft, a finite-time control method based on forced oscillation is proposed. To characterize the dynamics of deep stall state, bifurcation theory is employed for analysis, and the region of attraction boundaries is determined through backward-time integration. To generate precise recovery commands from mechanistic perspective, an extended bifurcation analysis is conducted, and forced oscillation commands corresponding to unstable bifurcation points are incorporated into the controller design. To handle time-varying disturbances and aerodynamic parameter perturbations during deep stall, a disturbance observer is designed to estimate lumped uncertainties while neural networks compensate for model uncertainties, and the deep stall recovery controller is obtained by combining with angle of attack tracking error feedback. The system signals involved in the Lyapunov function are proved to be bounded and the sliding mode surface converges in finite time. Simulation results show that the proposed method can reduce the fighter aircraft’s angle of attack to a safe zone while maintaining stable controllability, and achieve rapid and smooth recovery from deep stall conditions.

参考文献

[1] 张子军, 赵彤, 孙烨, 等. 飞机大迎角飞行问题研究综述[J]. 航空工程进展202213(3): 74-85.
  ZHANG Z J, ZHAO T, SUN Y, et al. Review of the study on high-angle-of-attack flight problems of aircraft[J]. Advances in Aeronautical Science and Engineering202213(3): 74-85 (in Chinese).
[2] 于目航, 王霞, 杨林, 等. 面向战机大迎角机动过程的智能学习控制[J]. 自动化学报202450(4): 719-730.
  YU M H, WANG X, YANG L, et al. Intelligent learning control for fighter maneuvers at high angle of attack[J]. Acta Automatica Sinica202450(4): 719-730 (in Chinese).
[3] 陈永亮, 沈宏良, 刘昶. 飞机深失速改出特性分析与控制[J]. 南京航空航天大学学报200739(4): 435-439.
  CHEN Y L, SHEN H L, LIU C. Analysis and control of aircraft deep stall recovery characteristics[J]. Journal of Nanjing University of Aeronautics Astronautics200739(4): 435-439 (in Chinese).
[4] 付军泉, 史志伟, 耿玺, 等. 基于试验分岔分析的翼身融合飞行器纵向稳定性[J]. 航空学报202243(1): 124931.
  FU J Q, SHI Z W, GENG X, et al. Longitudinal stability of blended-wing-body aircraft based on experimental bifurcation analysis[J]. Acta Aeronautica et Astronautica Sinica202243(1): 124931 (in Chinese).
[5] ILOPUTAIFE O I. Design of deep stall protection for the C-17A[J]. Journal of Guidance, Control, and Dynamics199720(4): 760-767.
[6] 陶呈纲, 林传健, 铁钰嘉, 等. 透过NGAD的可控性评估准则研究[J]. 航空学报202445(17): 530083.
  TAO C G, LIN C J, TIE Y J, et al. Controllability evaluation criteria research based on NGAD[J]. Acta Aeronautica et Astronautica Sinica202445(17): 530083 (in Chinese).
[7] JIANG Y, LI D C, KAN Z, et al. Bifurcation analysis of wing rock and routes to chaos of a low aspect ratio flying wing[J]. Nonlinear Dynamics2024112(23): 21491-21508.
[8] SALAHUDDEN, GHOSH A K. Robust control design based aircraft flat-spin recovery using optimally deflected novel deployable fin[J]. Aerospace Science and Technology2021115: 106823.
[9] DEFAZIO R L. Final committee report on the design, development, and certification of the boeing-737-max[EB/OL]. (2020-12-22)[2025-05-06]. .
[10] SALAHUDDEN S, DAS A T, GHOSH A K. Sliding-mode control and strategic thrust-vectoring based aircraft flat spin recovery with altitude loss minimization[J]. IEEE Transactions on Aerospace and Electronic Systems202258(4): 3271-3282.
[11] XU J L, GU R, HUANG S. Exploring the impact of vector thrust on aircraft maneuverability utilizing bypass dual throat nozzle technology[J]. Aerospace Science and Technology2025156: 109765.
[12] ASADI M, BEHNAMGOL V, VALI A R. Finite time thrust vector tracker based on a new smooth second order adaptive sliding mode controller[J]. International Journal of Research and Technology in Electrical Industry20243(1): 299-308.
[13] GOUSMAN K, LOSCHKE R, ROONEY R, et al. Aircraft deep stall analysis and recovery: AIAA-1991-2888[R]. Reston: AIAA, 1991.
[14] NGUYEN L T. Simulator study of stall/post-stall characteristics of a fighter airplane with relaxed longitudinal static stability[M]. Washington, D.C.: NASA, 1979: 49-58.
[15] MONTGOMERY R C, MOUL M T. Analysis of deep-stall characteristics of T-tailed aircraft configurations and some recovery procedures[J]. Journal of Aircraft19663(6): 562-566.
[16] GARCíA PéREZ J, GHADAMI A, SANCHES L, et al. Data-driven bifurcation analysis of experimental aeroelastic systems using preflutter measurements[J]. AIAA Journal202462(5): 1906-1914.
[17] NGUYEN D H, LOWENBERG M H, NEILD S A. A graphical approach to examining classical extremum seeking using bifurcation analysis[J]. IEEE Transactions on Control Systems Technology202331(3): 1324-1335.
[18] NGUYEN D H, GOMAN M G, LOWENBERG M H, et al. Evaluating longitudinal unsteady aerodynamic effects in stall for a T-tail transport model[J]. Journal of Aircraft202259(4): 964-976.
[19] XU K, YIN Y, YANG Y X, et al. Bifurcation analysis of dual-sidestay landing gear locking performance considering joint clearance[J]. Chinese Journal of Aeronautics202235(7): 209-226.
[20] WANG X, YU M H, YANG L, et al. Airflow angles estimation-based finite-time adaptive neural control for aircraft at high-angle-of-attack maneuvers[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems202555(8): 5126-5136.
[21] KOLB S, MONTAGNIER O, HéTRU L, et al. Real-time detection of an aircraft deep stall and recovery procedure[J]. Journal of Guidance, Control, and Dynamics201942(5): 1185-1194.
[22] CUNIS T, CONDOMINES J P, BURLION L. Sum-of-squares flight control synthesis for deep-stall recovery[J]. Journal of Guidance, Control, and Dynamics202043(8): 1498-1511.
[23] FAN Q Y, CAI M Y, XU B. An improved prioritized DDPG based on fractional-order learning scheme[J]. IEEE Transactions on Neural Networks and Learning Systems202536(4): 6873-6882.
[24] NGUYEN D H, LOWENBERG M H, NEILD S A. Derivation of control inputs for deep stall recovery using nonlinear frequency analysis[J]. The Aeronautical Journal2023127: 232-254.
[25] MING R C, LIU X X, XU X L, et al. Application of reinforcement learning in deep-stall recovery[J]. IEEE Transactions on Aerospace and Electronic Systems202561(4): 10581-10594.
[26] NGUYEN D H, LOWENBERG M H, NEILD S A. Analysing dynamic deep stall recovery using a nonlinear frequency approach[J]. Nonlinear Dynamics2022108(2): 1179-1196.
[27] MOUSTAFA E, ABOU-ZALAM B, SOBAIH A A, et al. Optimized fuzzy fractional-order controller for a nonlinear chaos system with period-doubling bifurcation analysis[J]. International Journal of Control, Automation and Systems202321(10): 3492-3503.
[28] WANG X, YANG L, XU B, et al. Fixed-time robust neural learning control for nonlinear strict-feedback systems with prescribed performance[J]. International Journal of Robust and Nonlinear Control202535(3): 1269-1280.
[29] YU J P, SHI P, ZHAO L. Finite-time command filtered backstepping control for a class of nonlinear systems[J]. Automatica201892: 173-180.
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