精细抗干扰控制——从干扰不变性到适应可变性

谢一嘉; 李文硕; 朱玉凯; 崔洋洋; 郭雷

doi:10.16383/j.aas.c250211

精细抗干扰控制——从干扰不变性到适应可变性

doi: 10.16383/j.aas.c250211 cstr: 32138.14.j.aas.c250211

谢一嘉^1,,
李文硕^2,,
朱玉凯^3,,
崔洋洋^1,,
郭雷^{1, 2,}

1.
北京航空航天大学自动化科学与电气工程学院北京 100191
2.
北京航空航天大学杭州创新研究院杭州 310052
3.
北京航空航天大学宇航学院北京 102206

基金项目: 国家自然科学基金(62473016, 62373033, 62303019), 北京市自然科学基金(L252020), 国家资助博士后研究人员计划(GZC20252746), 中央高校基本科研业务费资助

详细信息

作者简介:
谢一嘉：北京航空航天大学自动化科学与电气工程学院博士后. 2017年获得南京理工大学学士学位, 2024年获得北京航空航天大学博士学位. 主要研究方向为抗干扰控制, 奇异摄动系统, 模型预测控制. E-mail: yjxiebuaa@126.com

李文硕：北京航空航天大学杭州创新研究院副研究员. 2012年获得山东大学学士学位, 2020年获得北京航空航天大学博士学位. 主要研究方向为自主导航, 抗干扰状态估计, 多源信息融合. E-mail: wslibuaa@126.com

朱玉凯：北京航空航天大学宇航学院副教授. 2020年获得北京航空航天大学博士学位. 主要研究方向为复合抗干扰控制及其应用, 航天器自主机动控制. E-mail: ykzhubuaa@126.com

崔洋洋：北京航空航天大学自动化科学与电气工程学院副教授. 2022年获得北京航空航天大学博士学位. 主要研究方向为干扰估计与补偿, 先进控制理论及其在机电系统、飞行器等领域的工程应用. E-mail: yangyangcui@buaa.edu.cn

郭雷：中国科学院院士, 北京航空航天大学自动化科学与电气工程学院教授. 1997年获得东南大学博士学位. 主要研究方向为无人系统仿生智能, 抗干扰控制理论及其应用, 仿生自主导航. 本文通信作者. E-mail: lguo@buaa.edu.cn

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出版历程
- 收稿日期: 2025-05-11
- 录用日期: 2025-08-04
- 网络出版日期: 2025-09-08
- 刊出日期: 2025-10-29

Refined Anti-disturbance Control: From Disturbance Invariance to Adaptive Variability

XIE Yi-Jia^1
,,
LI Wen-Shuo^2
,,
ZHU Yu-Kai^3
,,
CUI Yang-Yang^1
,,
GUO Lei^{1, 2
,}

1.
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191
2.
Hangzhou Innovation Institute, Beihang University, Hangzhou 310052
3.
School of Astronautics, Beihang University, Beijing 102206

Funds: Supported by National Natural Science Foundation of China (62473016, 62373033, 62303019), Beijing Natural Science Foundation (L252020), National Postdoctoral Researcher Program (GZC20252746), and the Fundamental Research Funds for the Central Universities

More Information

Author Bio:
XIE Yi-Jia　Postdoctor at the School of Automation Science and Electrical Engineering, Beihang University. He received his bachelor degree from Nanjing University of Science and Technology in 2017, and his Ph.D. degree from Beihang University in 2024. His research interest covers anti-disturbance control, singularly perturbed systems, and model predictive control

LI Wen-Shuo　Associate researcher at the Hangzhou Innovation Institute, Beihang University. He received his bachelor degree from Shandong University in 2012, and his Ph.D. degree from Beihang University in 2020. His research interest covers autonomous navigation, anti-disturbance state estimation, and multi-source information fusion

ZHU Yu-Kai　Associate professor at the School of Astronautics, Beihang University. He received his Ph.D. degree from Beihang University in 2020. His research interest covers composite anti-disturbance control and its applications, and autonomous maneuvering control of spacecraft

CUI Yang-Yang　Associate professor at the School of Automation Science and Electrical Engineering, Beihang University. He received his Ph.D. degree from Beihang University in 2022. His research interest covers disturbance estimation and compensation, advanced control theory, and its engineering applications in electromechanical systems, aircraft and related fields

GUO Lei　Academician at Chinese Academy of Sciences, professor at the School of Automation Science and Electrical Engineering, Beihang University. He received his Ph.D. degree from Southeast University in 1997. His research interest covers bionic intelligence for unmanned systems, anti-disturbance control theory and its applications, and bionic autonomous navigation. Corresponding author of this paper

摘要

摘要: 抗干扰是控制科学和智能科学的基本主题之一. 长期以来, 干扰不变性被视为抗干扰控制方法的一个设计准则. 然而, 干扰不变性设计带来的控制代价易被忽视, 且往往不满足执行机构和信息拓扑等系统软硬件限制. 本文在干扰不变性准则的基础上, 提出干扰适应可变性准则和设计思想. 主要实现途径包括: 干扰深耦合建模、干扰可抗/可用度量化、复合抗干扰控制、干扰主动和精细利用、基于抗扰能力量化的系统重构优化等. 在此基础上, 进一步提出系统进化设计、进化智能和智能系统工程的思想, 从“任务目标−干扰因素−系统资源”一体化的角度提高动态适配性, 实现闭环系统的行为进化和形态进化. 干扰适应可变性准则突破了传统干扰不变性准则的藩篱, 实现了从“抗干扰”到“识干扰”、“用干扰”的干扰精细控制理论跨越, 为精细抗干扰控制理论和智能系统工程实践提供了新的理论支撑、研究视角和技术途径.
- 精细抗干扰控制 /
- 复合抗干扰控制 /
- 干扰利用 /
- 干扰适应可变性准则 /
- 智能系统工程
Abstract: Anti-disturbance is one of the basic themes of control science and intelligence science. The principle of disturbance invariance has been used as one of the design criteria for anti-disturbance control methods for a long time. However, the control cost caused by the principle of disturbance invariance design is often ignored, and the limitations of system software and hardware, including the constraints of actuator and information topology, are difficult to be satisfied. To this end, this survey proposes the design idea and principle of adaptive variability under disturbance based on the principle of disturbance invariance. The main implementation methods include: Disturbance deeply-coupled modeling, disturbance resistance/availability quantification, composite anti-disturbance control, active and refined disturbance utilization, and anti-disturbance capability quantification based system reconstruction and optimization, etc. On this basis, the ideas of system evolutionary design, evolutionary intelligence, and intelligent system engineering are further proposed. The dynamic adaptability is improved from the integrated perspective of “task goals-disturbance factors-system resources”, and further achieving closed-loop behavioral and morphological evolution. The principle of adaptive variability under disturbance breaks through the barriers of the traditional principle of disturbance invariance. It realizes the theoretical leap of refined anti-disturbance control theory from “anti-disturbance” to “disturbance recognition” and “disturbance utilization”. It provides new theoretical support, research perspectives, and technical approaches for refined anti-disturbance control theory and intelligent system engineering practice.
- Refined anti-disturbance control /
- composite anti-disturbance control /
- disturbance utilization /
- principle of adaptive variability under disturbance /
- intelligent system engineering

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图 1 从干扰不变性到适应可变性与系统进化设计

Fig. 1 From disturbance invariance to adaptive variability and system evolvable design

下载: 全尺寸图片幻灯片

图 2 干扰适应可变性准则的设计框架

Fig. 2 Design framework for the principle of adaptive variability under disturbance

下载: 全尺寸图片幻灯片

表 1 干扰主动和精细利用的典型应用

Table 1 Typical applications of active and refined disturbance utilization

问题	方法	效果
无人机控制^[55]	利用气动阻力干扰实现增稳	跟踪精度提升$ 16.9\% $, 不引入额外能耗
卫星姿态对准^[56]	利用耦合干扰改善误差收敛特性	稳态精度提升91.64%, 能耗降低71.69%

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