高超声速飞行器干扰补偿与解耦最优反步控制策略

高海燕; 陈智超; 蔡远利; 唐伟强

doi:10.7652/xjtuxb202510016

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高超声速飞行器干扰补偿与解耦最优反步控制策略

更新时间：2025-09-28

- 高超声速飞行器干扰补偿与解耦最优反步控制策略
- Disturbance Compensation and Decoupling Optimal Backstepping Control Strategy for Hypersonic Vehicle
- 西安交通大学学报 2025年59卷第10期页码：170-179
- 作者机构：
- 作者简介：
  
  作者简介：高海燕（1986—），女，讲师，硕士生导师。
- 基金信息：
  
  国家自然科学基金资助项目。(62463017)
- DOI：10.7652/xjtuxb202510016
  中图分类号： TP273
- 收稿：2025-01-09，
  
  纸质出版：2025-10-10
- 稿件说明：
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高海燕, 陈智超, 蔡远利, 等. 高超声速飞行器干扰补偿与解耦最优反步控制策略[J]. 西安交通大学学报, 2025,59(10):170-179.

GAO Haiyan, CHEN Zhichao, CAI Yuanli, et al. Disturbance Compensation and Decoupling Optimal Backstepping Control Strategy for Hypersonic Vehicle[J]. Journal of Xi'an Jiaotong University, 2025, 59(10): 170-179.
高海燕, 陈智超, 蔡远利, 等. 高超声速飞行器干扰补偿与解耦最优反步控制策略[J]. 西安交通大学学报, 2025,59(10):170-179. DOI： 10.7652/xjtuxb202510016.

GAO Haiyan, CHEN Zhichao, CAI Yuanli, et al. Disturbance Compensation and Decoupling Optimal Backstepping Control Strategy for Hypersonic Vehicle[J]. Journal of Xi'an Jiaotong University, 2025, 59(10): 170-179. DOI： 10.7652/xjtuxb202510016.

摘要

针对高超声速飞行器不确定性问题，提出了一种融合最优控制、反步控制和极限学习机干扰观测器的新型复合控制策略。为降低控制器设计复杂度，将其纵向模型解耦为速度子系统和高度子系统。速度子系统通过误差状态变换构建线性化模型，设计二次型性能指标下的最优反馈控制律；高度子系统采用反步控制框架，在第一步虚拟控制中引入最优二次调节理论以提升动态跟踪性能。将系统的多源干扰视为复合加性干扰，设计极限学习机神经网络进行估计，并利用估计值在控制律中进行干扰补偿以提升系统鲁棒性，通过李雅普诺夫稳定性理论证明了闭环系统的半全局一致最终稳定性。仿真结果表明：与传统反步控制相比，所提方法的速度和高度响应时间分别缩短了99.61%、4.60%；速度和高度跟踪精度分别提升了99.91%、85.64%；与基于扩张状态观测器的控制方案相比，极限学习机干扰观测器在速度和高度跟踪精度上优势显著，分别实现83.3%、71.3%的提升；相较于固定时间干扰观测器，速度跟踪精度提升幅度达88.76%，高度跟踪精度提升了36.14%。结果充分验证了所提方法在动态响应性能与抗干扰能力方面的优势。

Abstract

To address the uncertainties associated with hypersonic vehicles

a novel composite control strategy that integrates optimal control

backstepping control

and an extreme learning machine (ELM) disturbance observer is proposed. To reduce the complexity of the controller design

the longitudinal model is decoupled into a velocity subsystem and an altitude subsystem. For the velocity subsystem

a linearized model is constructed through error state transformation

and an optimal feedback control law is designed based on a quadratic performance index. The altitude subsystem adopts a backstepping control framework

where optimal quadratic regulation theory is introduced in the first virtual control step to enhance dynamic tracking performance. Multi-source disturbances are treated as composite additive disturbances and estimated using an ELM neural network

with the estimated values incorporated into the control law for disturbance compensation to improve system robustness. The semi-global uniform ultimate boundedness of the closed-loop system is proven via Lyapunov stability theory. Simulation results demonstrate that

compared to traditional backstepping control

the proposed method reduces response times by 99.61% for velocity and 4.60% for altitude

while improving tracking accuracy for velocity and altitude by 99.91% and 85.64%

respectively. When compared to an extended state observer (ESO)-based control scheme

the ELM disturbance observer exhibits significant advantages in velocity and altitude tracking accuracy

achieving improvements of 83.3% and 71.3%

respectively. Furthermore

it outperforms fixed-time disturbance observers with an 88.76% increase in velocity tracking accuracy and a 36.14% improvement in altitude tracking accuracy. These results validate the superiority of the proposed method in terms of dynamic response performance and disturbance rejection capability.

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references

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