高超声速变形飞行器自适应抗扰与抗饱和最优控制

顾鑫; 韦常柱; 刘哲; 马鑫海; 黄浩

doi:10.16383/j.aas.c260047

高超声速变形飞行器自适应抗扰与抗饱和最优控制

doi: 10.16383/j.aas.c260047 cstr: 32138.14.j.aas.c260047

顾鑫^1,,
韦常柱^1,,
刘哲^1,,
马鑫海^1,,
黄浩^1,

1.
哈尔滨工业大学航天学院哈尔滨 150001

基金项目: 国家自然科学基金(62373124)资助

详细信息

作者简介:
顾鑫：哈尔滨工业大学博士研究生. 主要研究方向为飞行器控制, 人工智能理论及应用. E-mail: nync396@126.com

韦常柱：哈尔滨工业大学教授. 主要研究方向为飞行器控制, 人工智能理论及应用, 先进制导与弹道优化. E-mail: weichangzhu@hit.edu.cn

刘哲：哈尔滨工业大学研究员. 主要研究方向为飞行器控制, 先进制导与弹道优化. 本文通信作者. E-mail: liuzhe@hit.edu.cn

马鑫海：哈尔滨工业大学博士研究生. 主要研究方向为飞行器控制, 人工智能理论及应用. E-mail: 25B318070@stu.hit.edu.cn

黄浩：哈尔滨工业大学硕士研究生. 主要研究方向为飞行器控制, 人工智能理论及应用. E-mail: hit_huang_hao@163.com

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出版历程
- 收稿日期: 2026-01-20
- 录用日期: 2026-03-04
- 网络出版日期: 2026-04-27

Hypersonic Morphing Vehicle Adaptive Active Disturbance Rejection and Anti-saturation Optimal Control

GU Xin^1
,,
WEI Chang-Zhu^1
,,
LIU Zhe^1
,,
MA Xin-Hai^1
,,
HUANG Hao^1
,

1.
School of Astronautics, Harbin Institute of Technology, Harbin 150001

Funds: Supported by National Natural Science Foundation of China (62373124)

More Information

Author Bio:
GU Xin　Ph.D. candidate at Harbin Institute of Technology. His research interest include aircraft control, artificial intelligence theory and applications

WEI Chang-zhu　Professor at Harbin Institute of Technology. His research interest include aircraft control, artificial intelligence theory and applications, advanced guidance and trajectory optimization

LIU Zhe　Research Professor at Harbin Institute of Technology. His research interest include aircraft control, advanced guidance and trajectory optimization. Corresponding author of this paper

MA Xin-hai　Ph.D. candidate at Harbin Institute of Technology. His research interest include aircraft control, artificial intelligence theory and applications

HUANG Hao　Master student at Harbin Institute of Technology. His research interest include aircraft control, artificial intelligence theory and applications

摘要

摘要: 针对高超声速变形飞行器在变形过程中面临的模型不确定性、强外部扰动及执行器饱和问题,提出一种复合控制方法. 该方法集成预设时间扰动观测器、抗饱和辅助系统与自适应动态规划.首先, 设计基于模糊系统的预设时间扰动观测器, 实现对集总扰动的快速精确估计与前馈补偿; 其次, 引入动态抗饱和辅助变量, 在控制量饱和后调整收敛轨迹从而减轻饱和, 并在饱和结束后引导系统收敛, 保障系统闭环稳定性; 进一步, 构建包含跟踪误差与控制能耗的综合代价函数, 采用自适应动态规划在线逼近最优控制律, 通过输入到状态稳定性理论证明闭环系统所有信号一致最终有界. 仿真结果表明, 所提控制方法在强扰动与执行器饱和条件下, 能实现姿态跟踪误差的预设时间收敛, 相比传统的抗扰控制与纯数据驱动方法, 具备更快的动态响应、更高的跟踪精度与更优的饱和抑制能力.
- 高超声速变形飞行器 /
- 预设时间模糊扰动观测器 /
- 抗饱和控制 /
- 自适应动态规划
Abstract: This paper proposes a composite control method to solve the problems of model uncertainties, strong external disturbances, and actuator saturation faced by hypersonic morphing vehicles during morphing. The method integrates a prescribed-time disturbance observer, an anti-saturation auxiliary system, and adaptive dynamic programming. Firstly, a prescribed-time disturbance observer based on a fuzzy system is designed to achieve fast and accurate estimation of lumped disturbances and feedforward compensation; Secondly, dynamic anti-saturation auxiliary variables are introduced to adjust the convergence trajectory after control saturation, thereby mitigating saturation effects, and to guide the system to converge after saturation ends, ensuring the closed-loop stability of the system; Furthermore, a comprehensive cost function incorporating tracking error and control energy consumption is constructed. Adaptive dynamic programming is employed to approximate the optimal control law online, and the input-to-state stability theory is utilized to prove that all signals of the closed-loop system are uniformly ultimately bounded. Simulation results demonstrate that the proposed control method can achieve preset-time convergence of attitude tracking errors under strong disturbances and actuator saturation conditions. Compared with traditional disturbance rejection control and purely data-driven methods, it exhibits faster dynamic response, higher tracking accuracy, and superior saturation suppression capability.
- hypersonic morphing vehicle /
- prescribed-time fuzzy disturbance observer /
- anti-saturation control /
- adaptive dynamic programming

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图 1 变形飞行器示意图

Fig. 1 Schematic diagram of a morphing aircraft

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图 2 控制器示意图

Fig. 2 Schematic diagram of controller

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图 3 滚转角跟踪对比情况

Fig. 3 Comparison of roll angle tracking

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图 4 俯仰角跟踪对比情况

Fig. 4 Comparison of pitch angle tracking

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图 5 偏航角跟踪对比情况

Fig. 5 Comparison of yaw angle tracking

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图 6 滚转角速率对比情况

Fig. 6 Comparison of roll rate

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图 7 俯仰角速率对比情况

Fig. 7 Comparison of pitch rate

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图 8 偏航角速率对比情况

Fig. 8 Comparison of yaw rate

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图 9 滚转角误差对比情况

Fig. 9 Comparison of roll angle errors

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图 10 俯仰角误差对比情况

Fig. 10 Comparison of pitch angle errors

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图 13 俯仰力矩指令对比情况

Fig. 13 Comparison of pitch torque commands

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图 14 偏航力矩指令对比情况

Fig. 14 Comparison of yaw torque commands

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图 11 偏航角误差对比情况

Fig. 11 Comparison of yaw angle errors

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图 12 滚转力矩指令对比情况

Fig. 12 Comparison of roll torque commands

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表 1 基于模糊系统的预设时间扰动观测器参数

Table 1 Parameters of prescribed-time disturbance observer based on fuzzy system

参数	符号	取值
预设时间	$ T_d $	0.5
增益系数	$ k_1、k_2 $	1.5
预设时间指数	$ r_1、r_3 $	0.8
模糊系统学习率	$ \gamma_1、\gamma_2、\gamma_3 $	0.01
参数衰减系数	$ \kappa $	0.01
隶属函数中心范围	$ L $	5
隶属函数宽度	$ \sigma_{ij} $	$ L/3 $

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表 2 预设时间抗饱和辅助系统参数

Table 2 Parameters of preset time anti-saturation auxiliary system

参数	符号	取值
预设时间	$ T_c $	0.6
增益系数	$ a_c、b_c $	1.2
收敛指数	$ r_c $	0.7

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表 3 预设性能控制律参数

Table 3 Parameters of preset performance control law

参数	符号	取值
滑模系数矩阵	$ c $	$ {\rm{diag}}\{2,\;2,\;2\} $
滑模趋近增益矩阵	$ K_s $	$ {\rm{diag}}\{5,\;5,\;5\} $
滑模指数	$ \alpha_0 $	0.5
预设性能初始边界	$ \lambda_0 $	5
预设性能稳态边界	$ \lambda_{\infty} $	0.2
收敛速率	$ \beta_i $	2

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表 4 基于ADP的控制律参数

Table 4 Parameters of control law based on ADP

参数	符号	取值
Critic网络稳定参数	$ \alpha_{c1} $	0.01
Critic网络学习率	$ \alpha_{c2} $	0.01
折扣因子	$ \gamma $	0.99
控制权重矩阵	$ R $	$ {\rm{diag}}\{1,\;1,\;1\} $
状态权重矩阵	$ Q $	$ {\rm{diag}}\{5,\;5,\;5\} $
缓冲区大小	$ M $	100

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表 5 对比方法控制器参数

Table 5 Parameters of comparison method controller

控制器	参数值
抗扰控制器	$ \xi = 0.75 $、$ \omega_o = 5 $、$ \omega_b = 8 $、
	$ \beta_1 = 16 $、$ \beta_2 = 64 $
强化学习控制器	$ \mu_a = 0.01 $、$ \mu_c = 0.001 $,
	$ \rho_a = 0.9 $、$ \rho_c = 0.9 $、
	$ R = {\rm{diag}}\{1,\;1,\;1\} $、
	$ Q = {\rm{diag}}\{5,\;5,\;5,\;1,\;1\} $

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