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资助项目(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

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　Postdoc at the School of Automation Science and Electrical Engineering, Beihang University. He received the 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. 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”的一体化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 evolvable 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 achieveing 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

HTML全文

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 pincipe of adaptive variablity 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	姿态对准误差降低, 收敛时间缩短

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参考文献(67)

[1]	Schipanov G. Theory and methods of designing automatic regulators. Automatika in Telemekhanika, 1939, 4: 49−66
[2]	Luzin N N. Absolute invariance and ϵ-invariance in the theory of differential equations. Dokl. Akad. Nauk SSSR, 1946, 51(4): 251−253
[3]	Petrov B N. The invariance principle and the conditions for its application during the calculation of linear and non-linear systems. In: Proceedings of the IFAC Proceedings Volumes. Moscow, the Soviet Union: Elsevier, 1960. 127-135
[4]	Kulebakin V S. The theory of invariance of regulating and control systems. In: Proceedings of IFAC Proceedings Volumes. Moscow, the Soviet Union: Elsevier, 1960. 116-126
[5]	乌兰诺夫(苏), 胡保生(译). 扰动调节. 上海: 上海科学技术出版社, 1963 Уланов, Hu Bao-Sheng (Translated). Disturbance Adjusting. Shanghai: Shanghai Scientific & Technical Publishers, 1963
[6]	Ashby W R. An Introduction to Cybernetics. New York: Wiley, 1956
[7]	Preminger J, Rootenberg J. Some considerations relating to control systems employing the invariance principle. IEEE Transactions on Automatic Control, 1964, 9(3): 209−215 doi: 10.1109/TAC.1964.1105722
[8]	Prigogine I, Stengers I. The End of Certainty Simon and Schuster. New York: Editions Odile Jacob, 1997.
[9]	冯纯伯. 鲁棒控制系统设计. 南京: 东南大学出版社, 1995 Feng Chun-Bo. Design of Robust Control Systems. Nanjing: Southeast University Press, 1995
[10]	陈翰馥, 郭雷. 现代控制理论的若干进展及展望. 科学通报, 1998, 43(1): 1−7 doi: 10.3321/j.issn:0023-074X.1998.01.001 Chen Han-Fu, Guo Lei. Progress and prospects of modern control theory. Science Bulletin, 1998, 43(1): 1−7 doi: 10.3321/j.issn:0023-074X.1998.01.001
[11]	郭雷, 冯纯伯. 一类具有非线性不确定性系统的鲁棒H_∞控制. 控制理论与应用, 1999, 16(4): 619−624 doi: 10.3969/j.issn.1000-8152.1999.04.039 Guo Lei, Feng Chun-Bo. Robust H_∞ control for a class of systems with nonlinear uncertainties. Control Theory and Applications, 1999, 16(4): 619−624 doi: 10.3969/j.issn.1000-8152.1999.04.039
[12]	黄琳, 段志生. 控制科学中的复杂性. 自动化学报, 2003, 29(5): 748−754 Huang Lin, Duan Zhi-Sheng. Complexity in control science. Acta Automatica Sinica, 2003, 29(5): 748−754
[13]	郭雷. 不确定性动态系统的估计、控制与博弈. 中国科学: 信息科学, 2020, 50(9): 1327−1344 doi: 10.1360/SSI-2020-0277 Guo Lei. Estimation, control, and games of dynamical systems with uncertainty. Scientia Sinica Informationis, 2020, 50(9): 1327−1344 doi: 10.1360/SSI-2020-0277
[14]	郭雷, 李文硕, 崔洋洋, 朱玉凯, 章健淳, 余翔, 包为民. 动态闭环不确定性量化理论与智能无人系统应用. 中国科学: 技术科学, 2025, 55(1): 1−13 doi: 10.1360/SST-2024-0155 Guo Lei, Li Wen-Shuo, Cui Yang-Yang, Zhu Yu-Kai, Zhang Jian-Chun, Yu Xiang, Bao Wei-Min. Dynamic closed-loop uncertainty quantification theory with intelligent unmanned systems applications. Scientia Sinica Technologica, 2025, 55(1): 1−13 doi: 10.1360/SST-2024-0155
[15]	郭雷, 朱玉凯, 乔建忠, 郭康, 包为民. 无人系统生存智能与安全、免疫、绿色控制技术. 航空学报, 2022, 43(10): 527129 doi: 10.7527/S1000-6893.2022.27129 Guo Lei, Zhu Yu-Kai, Qiao Jian-Zhong, Guo Kang, Bao Wei-Min. Survival intelligence and safety, immunity, and green control technologies for unmanned systems. Acta Aeronautica et Astronautica Sinica, 2022, 43(10): 527129 doi: 10.7527/S1000-6893.2022.27129
[16]	Tian G, Gao Z Q. From Poncelet's invariance principle to active disturbance rejection. In: Proceedings of the 2009 American Control Conference. St. Louis, USA: IEEE, 2009. 2451-2457
[17]	Guo L, Cao S Y. Anti-Disturbance Control for Systems with Multiple Disturbances. Boca Raton: CRC Press, 2013.
[18]	Guo L, Cao S Y. Anti-disturbance control theory for systems with multiple disturbances: A survey. ISA Transactions, 2014, 53(4): 846−849 doi: 10.1016/j.isatra.2013.10.005
[19]	Chen W H, Yang J, Guo L, Li S H. Disturbance-observer-based control and related methods—an overview. IEEE Transactions on Industrial Electronics, 2016, 63(2): 619−624
[20]	郭雷, 朱玉凯. 多源干扰系统复合自主抗干扰控制技术(中国科研信息化蓝皮书). 北京: 电子工业出版社, 2020. 210-220 Guo Lei, Zhu Yu-Kai. Composite Autonomous Anti-Disturbance Control for Systems with Multiple Disturbances (Chapter of Chinese E-Science Blue Book 2020). Beijing: Publishing House of Electronics Industry, 2020. 210-220
[21]	温婷, 何奎, 宋薇萍. 科技产业联动2023外滩大会“探路”可持续发展未来. 上海证券报, 2023, DOI: 10.28719/n.cnki.nshzj.2023.003834 Wen Ting, He Kui, Song Wei-Ping. Technology and industry collaborate at the 2023 InClusion Conference on the Bund to explore a sustainable future. Shanghai Securities News, 2023, DOI: 10.28719/n.cnki.nshzj.2023.003834
[22]	Michael I. Jordan. Statistical contract theory [Online], available: https: //www.cirm-math.fr/RepOrga/2879/Slides/Jordan_cirm.pdf, Augest 9, 2025
[23]	Bhattacharyyta S P. Disturbance rejection in linear systems. International Journal of Systems Science, 1974, 5(7): 633−637 doi: 10.1080/00207727408920129
[24]	Bhattacharyyta S P. Compensator design based on the invariance principle. IEEE Transactions on Automatic Control, 1975, 20(5): 708−711 doi: 10.1109/TAC.1975.1101044
[25]	Guo L, Li W S, Zhu Y K, Yu X, Wang Z D. Composite disturbance filtering: A novel state estimation scheme for systems with multisource, heterogeneous, and isomeric disturbances. IEEE Open Journal of the Industrial Electronics Society, 2023, 4: 387−400 doi: 10.1109/OJIES.2023.3317271
[26]	Davison E J, Smith H W. Pole assignment in linear time-invariant multivariable systems with constant disturbances. Automatica, 1971, 7(4): 489−498 doi: 10.1016/0005-1098(71)90099-9
[27]	Davison E J. The output control of linear time-invariant multivariable systems with unmeasurable arbitrary disturbances. IEEE Transactions on Automatic Control, 1972, 17(5): 621−630 doi: 10.1109/TAC.1972.1100084
[28]	Johnson C D. Optimal control of the linear regulator with constant disturbances. IEEE Transactions on Automatic Control, 1968, 13(4): 416−421 doi: 10.1109/TAC.1968.1098947
[29]	Johnson C D. Accommodation of external disturbances in linear regulator and servomechanism problems. IEEE Transactions on Automatic Control, 1971, 16(6): 635−644 doi: 10.1109/TAC.1971.1099830
[30]	Ohishi K, Ohnishi K, Miyachi K. Torque-speed regulation of DC motor based on load torque estimation method. In: Proceedings of the JIEE/INternational Power Electronics Conference. Tokyo, Japan, 1983. 1209-1218
[31]	Chen W H, Ballance D J, Gawthrop P J, O'Reilly J. A nonlinear disturbance observer for robotic manipulators. IEEE Transactions on industrial Electronics, 2000, 47(4): 932−938 doi: 10.1109/41.857974
[32]	Han J Q. From PID to active disturbance rejection control. IEEE Transactions on Industrial Electronics, 2009, 56(3): 900−906 doi: 10.1109/TIE.2008.2011621
[33]	Gao Z Q. Active disturbance rejection control: A paradigm shift in feedback control system design. In: Proceedings of the 2006 American Control Conference. Minneapolis, USA, 2006. 2399-2405
[34]	Guo B Z, Zhao Z L. On convergence of the nonlinear active disturbance rejection control for MIMO systems. SIAM Journal on Control and Optimization, 2013, 51(2): 1727−1757 doi: 10.1137/110856824
[35]	Cao M Y, Yang J, Li S H, Madonski R, Xue W C. Cascaded filter PID paradigm for error-based active disturbance rejection control: Equivalence, design, and implementation guidelines. IEEE Transactions on Industrial Electronics, DOI: 10.1109/TIE.2025.3559950
[36]	Deng J Q, Xue W C, Zhang L Y, Bao Q L, Mao Y. Disturbance-compression extended state observer with noise insensitivity: Application to electro-optical tracking system. IEEE Transactions on Automation Science and Engineering, 2025, 22: 17761−17777 doi: 10.1109/TASE.2025.3585348
[37]	Chen W H, Rhodes C, Liu C J. Dual control for exploitation and exploration (DCEE) in autonomous search. Automatica, 2021, 133: 109851 doi: 10.1016/j.automatica.2021.109851
[38]	Li S H, Yang J, Iwasaki M, Chen W H. Hierarchical disturbance/uncertainty estimation and attenuation for integrated modeling and motion control: Overview and perspectives. IEEE/ASME Transactions on Mechatronics, DOI: 10.1109/TMECH.2024.3515084
[39]	Guo L, Chen W H. Disturbance attenuation for a class of nonlinear systems via disturbance-observer-based approach. In: Proceedings of the IFAC Proceedings Volumes. Barcelona, Spain: Elsevier, 2002. 19-24
[40]	Guo L, Chen W H. Disturbance attenuation and rejection for systems with nonlinearity via DOBC approach. International Journal of Robust and Nonlinear Control, 2005, 15(3): 109−125 doi: 10.1002/rnc.978
[41]	Hurme E, Lenzi I, Wikelski M, Wild T A, Dechmann D K N. Bats surf storm fronts during spring migration. Science, 2025, 387(6729): 97−102 doi: 10.1126/science.ade7441
[42]	罗战虎. 地效飞行器发展综述. 科技创新导报, 2021, 18(09): 17−22 Luo Zhan-Hu. Development overview of the ground effect vehicles. Science and Technology Innovation Herald, 2021, 18(09): 17−22
[43]	Benzi R, Sutera A, Vulpiani A. The mechanism of stochastic resonance. Journal of Physics A: Mathematical and General, 1981, 14(11): 453−457 doi: 10.1088/0305-4470/14/11/006
[44]	Gammaitoni L, Hanggi P, Jung P, Marchesoni F. Stochastic resonance. Reviews of Modern Physics, 1998, 70(1): 223−287 doi: 10.1103/RevModPhys.70.223
[45]	Chapeau B F. Noise-aided nonlinear Bayesian estimation. Physical Review E, 2002, 66(3): 032101
[46]	Chapeau B F, Rousseau D. Noise-enhanced performance for an optimal Bayesian estimator. IEEE Transactions on Signal Processing, 2004, 52(5): 1327−1334 doi: 10.1109/TSP.2004.826176
[47]	Meissner P, Witrisal K. Multipath-assisted single-anchor indoor localization in an office environment. In: Proceedings of the 2012 19th International Conference on Systems, Signals and Image Processing (IWSSIP). Vienna, Austria: IEEE, 2012.
[48]	Witrisal K, Meissner P, Leitinger E, Shen Y, Gustafson C, Tufvesson F, et al. High-accuracy localization for assisted living: 5G systems will turn multipath channels from foe to friend. IEEE Signal Processing Magazine, 2016, 33(2): 59−70 doi: 10.1109/MSP.2015.2504328
[49]	Wang T Y, Li Y X, Liu J C, Hu K K, Shen Y. Multipath-assisted single-anchor localization via deep variational learning. IEEE Transactions on Wireless Communication, 2024, 23(8): 9113−9128 doi: 10.1109/TWC.2024.3359047
[50]	Gigi S, Tangirala A K. Quantification of interaction in multiloop control systems using directed spectral decomposition. Automatica, 2013, 49(5): 1174−1183 doi: 10.1016/j.automatica.2013.01.061
[51]	Guo Z Y, Zhou J, Guo J G, Cieslak J, Chang J. Coupling-characterization-based robust attitude control scheme for hypersonic vehicles. IEEE Transactions on Industrial Electronics, 2017, 64(8): 6350−6361 doi: 10.1109/TIE.2017.2682031
[52]	Guo Z Y, Guo J G, Zhou J, Chang J. Robust tracking for hypersonic reentry vehicles via disturbance estimation-triggered control. IEEE Transactions on Aerospace and Electronic Systems, 2019, 56(2): 1279−1289
[53]	Zhang M H, Jing X J. Energy-saving robust saturated control for active suspension systems via employing beneficial nonlinearity and disturbance. IEEE Transactions on Cybernetics, 2021, 52(10): 10089−10100
[54]	Huang Z G, Chen M, Shi P. Disturbance utilization-based tracking control for the fixed-wing UAV with disturbance estimation. IEEE Transactions on Circuits and Systems I: Regular Papers, 2022, 70(3): 1337−1349
[55]	Jia J D, Guo K X, Yu X, Zhao W H, Guo L. Accurate high-maneuvering trajectory tracking for quadrotors: A drag utilization method. IEEE Robotics and Automation Letters, 2022, 7(3): 6966−6973 doi: 10.1109/LRA.2022.3176449
[56]	Teng H, Lu Y K, Xia P F, Qiao J Z, Guo L. Refined disturbance utilization-based green control for spacecraft with composite actuator disturbances. IEEE/ASME Transactions on Mechatronics, DOI: 10.1109/TMECH.2025.3563132
[57]	Zhou X B, Yu X, Guo K X, Zhou S C, Guo L, Zhang Y M, et al. Safety flight control design of a quadrotor UAV with capability analysis. IEEE Transactions on Cybernetics, 2021, 53(3): 1738−1751
[58]	Gu Y P, Guo K X, Zhao C L, Yu X, Guo L. Fast reactive mechanism for desired trajectory attacks on unmanned aerial vehicles. IEEE Transactions on Industrial Informatics, 2022, 19(8): 8976−8984
[59]	Meng Y, Qiao J Z, Zhu Y K, Teng H, Zhang J C. Remaining useful life prediction for spacecraft actuator based on multiplicative fault observer. IEEE Transactions on Aerospace and Electronic Systems, 2023, 59(6): 8489−8501 doi: 10.1109/TAES.2023.3306332
[60]	Zhang J C, Liu T Y, Qiao J Z. Solving a reliability-performance balancing problem for control systems with degrading actuators under model predictive control framework. Journal of the Franklin Institute, 2022, 359(9): 4260−4287 doi: 10.1016/j.jfranklin.2022.04.007
[61]	Bian J, Zhang J C, Guo K X, Li W S, Yu X, Guo L. Risk-aware path planning using CVaR for quadrotors. In: Proceedings of the 2023 6th International Symposium on Autonomous Systems (ISAS). Nanjing, China: IEEE, 2023. 1-6
[62]	Guo L, Zhu Y K, Qiao J Z, Wang C L. Composite anti-disturbance dynamic regulation for systems with multiple disturbances: From stability to balance. In: Proceedings of the 2021 33rd Chinese control and decision conference (CCDC). Kunming, China: IEEE, 2021. 5685-5690
[63]	Zhou S C, Wang M, Jia J D, Guo K X, Yu X, Zhang Y M, Guo L. Fault separation based on an excitation operator with application to a quadrotor UAV. IEEE Transactions on Aerospace and Electronic Systems, 2024, 60(4): 4010−4022 doi: 10.1109/TAES.2024.3371967
[64]	Jia J D, Zhang W Y, Guo K X, Wang J L, Yu X, Shi Y, Guo L. Evolver: Online learning and prediction of disturbances for robot control. IEEE Transactions on Robotics, 2023, 40: 382−402
[65]	Yang Y J, Bao Z Y, Qiao J Z, Zhu Y K, Guo L. Refined metamodel disturbance observer-based control for coarse pointing assembly under constraints. Guidance, Navigation and Control, 2024, 4(04): 2450017 doi: 10.1142/S2737480724500171
[66]	谭铁牛. 加强国际治理与合作推动人工智能向善向好. 当代世界, 2025(05): 4−9 doi: 10.3969/j.issn.1006-4206.2025.05.003 Tan Tie-Niu. Strengthening international governance and cooperation to promote the positive development of artificial intelligence. Contemporary World, 2025(05): 4−9 doi: 10.3969/j.issn.1006-4206.2025.05.003
[67]	曾凯, 王耀南, 谭浩然, 方遒, 汪渊, 袁礼伟. AI大模型驱动的具身智能人形机器人技术与展望. 中国科学: 信息科学, 2025, 55(05): 967−992 doi: 10.1360/SSI-2024-0350 Zeng Kai, Wang Yao-Nan, Tan Hao-Ran, Fang Qiu, Wang Yuan, Yuan Li-Wei. Prospects and technology of embodied intelligent humanoid robots driven by AI large models. Scientia Sinica Informationis, 2025, 55(05): 967−992 doi: 10.1360/SSI-2024-0350