ACTA AERONAUTICAET ASTRONAUTICA SINICA >
Research progress of intelligent control methods in space propulsion systems
Received date: 2023-02-01
Revised date: 2023-02-15
Accepted date: 2023-03-17
Online published: 2023-03-17
Supported by
Innovation Research Groups of the National Natural Science Foundation of China(T2221002)
The intelligentization of space propulsion technology has been a dream and pursuit for a long time and is an important way to improve the reliability and mission adaptability of space activities. With the development of artificial intelligence technologies, intelligent control technologies have been gradually applied in the space propulsion system. Based on the development of aerospace and artificial intelligence in various countries, this paper focuses on the intelligent control methods of aerospace propulsion systems. The research status and progress of intelligent control technologies for space propulsion systems are summarized, and several typical intelligent control technologies in space propulsion systems are reviewed. Then, the representative control systems of major aerospace countries as well as the development trends are discussed. Finally, according to the current intelligent control methods in the space propulsion system, the development trend of intelligent control methods in space propulsion systems is ananlyzed. Some suggestions are made to provide a useful reference for researchers engaged in the research on intelligent control methods for space propulsion systems.
Yuwei LIU , Yuqiang CHENG , Jianjun WU . Research progress of intelligent control methods in space propulsion systems[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2023 , 44(15) : 528505 -528505 . DOI: 10.7527/S1000-6893.2023.28505
1 | WANG H N, LIU N, ZHANG Y Y, et al. Deep reinforcement learning: A survey[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(12): 1726-1744. |
2 | ZHUANG Y T, WU F, CHEN C, et al. Challenges and opportunities: From big data to knowledge in AI 2.0[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(1): 3-14. |
3 | KALUARACHCHI T, REIS A, NANAYAKKARA S. A review of recent deep learning approaches in human-centered machine learning[J]. Sensors (Basel, Switzerland), 2021, 21(7): 2514. |
4 | LU Y. Artificial intelligence: A survey on evolution, models, applications and future trends[J]. Journal of Management Analytics, 2019, 6(1): 1-29. |
5 | 中华人民共和国国务院. 新一代人工智能发展规划[EB/OL]. (2017-07-20)[2023-01-13]. . |
The State Council of the People’s Republic of China. New Generation Artificial Intelligence Development Plan [EB/OL] (2017-07-20)[2023-01-13]. (in Chinese). | |
6 | 袁珩, 耿喆, 徐峰, 等. 美国人工智能战略布局与对外策略[J]. 科技管理研究, 2022, 42(12):34-39. |
YUAN H, GENG Z, XU F, et al. Artificial intelligence strategy layout and foreign strategies of the United States[J]. Science and Technology Management Research, 2022, 42(12):34-39 (in Chinese). | |
7 | 包为民. 航天智能控制技术让运载火箭 “会学习”[J]. 航空学报, 2021, 42(11): 525055. |
BAO W M. Space intelligent control technology enables launch vehicle to “self-learning”[J]. Acta Aeronautica et Astronautica Sinica, 2021, 42(11): 525055 (in Chinese). | |
8 | 泮斌峰, 张哲, 谭浩声. 美国喷气推进实验室人工智能技术发展现状与分析[J]. 中国航天, 2022(5): 525055. |
PAN B F, ZHANG Z, TAN H S. Development status and analysis of artificial intelligence technology in American jet propulsion laboratory[J]. Aerospace China, 2022(5): 8-14 (in Chinese). | |
9 | О развитии искусственного интеллекта в Российской Федерации[EB/OL] (2019-10-11) [2023-01-13]. . |
The Development of Artificial Intelligence in the Russian Federation. [EB/OL] (2019-10-11) [2023-01-13]. . | |
10 | 李锡宁.俄制定人工智能国家发展战略[EB/OL] (2019-10-28) [2023-01-13].. . |
Li X N. Russia formulates a national development strategy for artificial intelligence [EB/OL] (2019-10-28) [2023-01-13]. . (in Chinese). | |
11 | National Artificial Intelligence Initiative Act of 2020, H.R.6216, 116th Cong. (2020), [EB/OL] (2020-12-3) [2023-01-13] . |
12 | Commission European. 2021. European Union White Paper on Artificial Intelligence: A European Approach to Excellence and Trust. Brussels.[EB/OL] (2020-2-19) [2023-01-13]. . |
13 | Government UK . (2021). National AI Strategy. London [EB/OL] (2022-12-18) [2023-01-13]. . |
14 | Japan Aerospace Exploration Agency. (2017). Space Industry Outlook 2030. Tokyo. [EB/OL] (2017-5-29) [2023-01-13]. . |
15 | Federation Russian. (2019). National Strategy for the Development of Artificial Intelligence Until 2030. Moscow. [EB/OL] (2019-11-19) [2023-01-13] Russia Adopts National Strategy for Development of Artificial Intelligence-Jamestown. |
16 | 中华人民共和国中央人民政府. 2021中国航天[EB/OL]. (2022-01-28) [2023-01-13]. . |
The Central People’s Government of the People’s Republic of China. 2021 China Aerospace [EB/OL] (2022-01-28) [2023-01-13]. (in Chinese). | |
17 | 袁利. 面向不确定环境的航天器智能自主控制技术[J]. 宇航学报, 2021, 42(7):839-849. |
YUAN L. Spacecraft intelligent autonomous control technology toward uncertain environment[J]. Journal of Astronautics, 2021, 42(7):839-849 (in Chinese). | |
18 | 柴金宝, 陈雄, 周景亮, 等. 基于人工蜂群算法优化的燃气发生器 压强自适应模糊免疫PID控制[J]. 推进技术, 2019, 40(2): 441-448. |
CHAI J B, CHEN X, ZHOU J L, et al. Adaptive fuzzy immune PID control for gas generator pressure based on artificial bee colony algorithm optimization[J]. Journal of Propulsion Technology, 2019, 40(2): 441-448 (in Chinese). | |
19 | LEE C C. Intelligent control based on fuzzy logic and neural net theory: AA19126654[R]. Berkeley: University of California, 1991. |
20 | ATHANS M. Advances in control systems: Theory and applications, vol 1[J]. IEEE Transactions on Automatic Control, 1966, 11(3): 633-634. |
21 | MENDEL J M. Application of artificial intelligence techniques to a spacecraft control problem: NASA-CR-55[R]. Washington, D. C.: NASA, 1967. |
22 | FU K. Learning control systems and intelligent control systems: An intersection of artifical intelligence and automatic control[J]. IEEE Transactions on Automatic Control, 1971, 16(1): 70-72. |
23 | WALTZ M, FU K. A heuristic approach to reinforcement learning control systems[J]. IEEE Transactions on Automatic Control, 1965, 10(4): 390-398. |
24 | 德先生. 模式识别拓荒者是一位中国人[EB/OL]. (2018-08-24)[2023-01-13]. . |
Mr. De. Pattern recognition pioneer is a Chinese [EB/OL]. (2018-08-24)[2023-01-13]. (in Chinese). | |
25 | LEE C C. Fuzzy logic in control systems: fuzzy logic controller. Ⅰ[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1990, 20(2): 404-418. |
26 | LEE C C. Fuzzy logic in control systems: fuzzy logic controller. Ⅱ[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1990, 20(2): 419-435. |
27 | YASUNOBU S and HASEGAWA G, Evaluation of an automatic container crane operation system based on predictive fuzzy control[J]. Control Theory and Advanced Technology, 1986, 2(3):419-432. |
28 | XIANG X B, YU C Y, LAPIERRE L, et al. Survey on fuzzy-logic-based guidance and control of marine surface vehicles and underwater vehicles[J]. International Journal of Fuzzy Systems, 2018, 20(2): 572-586. |
29 | FENG G. A survey on analysis and design of model-based fuzzy control systems[J]. IEEE Transactions on Fuzzy Systems, 2006, 14(5): 676-697. |
30 | KOSHIYAMA A S, ESCOVEDO T, VELLASCO M M B R, et al. GPFIS-control: A fuzzy genetic model for control tasks[C]∥ 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). Piscataway: IEEE Press, 2014: 1953-1959. |
31 | MO J. Tools for intelligent control: Fuzzy controllers, neural networks and genetic algorithms[J]. Philosophical Transactions of the Royal Society of London Series A: Mathematical, Physical and Engineering Sciences, 2003, 361(1809): 1781-1808. |
32 | Lü X Q, WU Y B, LIAN J, et al. Energy management of hybrid electric vehicles: A review of energy optimization of fuel cell hybrid power system based on genetic algorithm[J]. Energy Conversion and Management, 2020, 205: 112474. |
33 | FEI J T, CHEN Y, LIU L, et al. Fuzzy multiple hidden layer recurrent neural control of nonlinear system using terminal sliding-mode controller[J]. IEEE Transactions on Cybernetics, 2022, 52(9): 9519-9534. |
34 | FEI J T, WANG Z, LIANG X, et al. Fractional sliding-mode control for microgyroscope based on multilayer recurrent fuzzy neural network[J]. IEEE Transactions on Fuzzy Systems, 2022, 30(6): 1712-1721. |
35 | LIANG H J, LIU G L, ZHANG H G, et al. Neural-network-based event-triggered adaptive control of nonaffine nonlinear multiagent systems with dynamic uncertainties[J]. IEEE Transactions on Neural Networks and Learning Systems, 2021, 32(5): 2239-2250. |
36 | 张立宪, 卢生奥, 韩铭昊, 等. 鲁棒强化学习及其在航天控制中的应用与展望[J]. 航天控制, 2021, 39(3): 3-11. |
ZHANG L X, LU S A, HAN M H, et al. Robust reinforcement learning and its application and prospect in aerospace control[J]. Aerospace Control, 2021, 39(3): 3-11 (in Chinese). | |
37 | PENG Z N, LUO R, HU J P, et al. Optimal tracking control of nonlinear multiagent systems using internal reinforce Q-learning[J]. IEEE Transactions on Neural Networks and Learning Systems, 2022, 33(8): 4043-4055. |
38 | SURRIANI A, WAHYUNGGORO O, CAHYADI A I. Reinforcement learning for cart pole inverted pendulum system[C]∥ 2021 IEEE Industrial Electronics and Applications Conference (IEACon). Piscataway: IEEE Press, 2021: 297-301. |
39 | KLOPF A H, The Hedonistic neuron[M]. Washington, D. C.: Hemisphere, 1982. |
40 | FUKUDA T, KUBOTA N. Intelligent robotic systems: Adaptation, learning, and evolution[J]. Artificial Life and Robotics, 1999, 3(1): 32-38. |
41 | LIN C M. Application of neural network in control problem[C]∥ 2007 International Conference on Machine Learning and Cybernetics. Piscataway: IEEE Press, 2007: 3465-3471. |
42 | SHI L, WANG XING CHENG. The application of neural network in nonlinear system[J]. Advanced Materials Research, 2011, 179-180: 128-134. |
43 | OMIDVAR O, ELLIOTT D L. Neural systems for control[M]. San Diego: Academic Press, 1997. |
44 | SPIELBERG S P K, GOPALUNI R B, LOEWEN P D. Deep reinforcement learning approaches for process control[C]∥ 2017 6th International Symposium on Advanced Control of Industrial Processes (AdCONIP). Piscataway: IEEE Press, 2017: 201-206. |
45 | SUTTON R S, BARTO A G. An introduction[M]. 2rd edition. Cambridge: The MIT Press, 2018. |
46 | BU?ONIU L, DE BRUIN T, TOLI? D, et al. Reinforcement learning for control: Performance, stability, and deep approximators[J]. Annual Reviews in Control, 2018, 46: 8-28. |
47 | BERTSEKAS D P. Reinforcement learning and optimal control[M]. Belmont: Athena Scientific, 2018. |
48 | OTTO E W, FLAGE R A, Control of combustion-chamber pressure and oxidant-fuel. ratio for a regeneratively cooled hydrogen-fluorine rocket engine: Technical note D-82[R]. Cleveland: NASA Lewis Research Center, 1959. |
49 | SEITZ P F, SEARLE R F. Space shuttle main engine control system: SAE PAPER 730927[R]. Washington, D.C.: NASA, 1973. |
50 | NEMETH E, ANDERSON R, OLS J, et al. Reusable rocket engine intelligent control system framework design: NASE-CR-187213[R]. Washington, D.C.: NASA, 1991. |
51 | NEMETH E, ANDERSON R, MARAM J, et al. An advanced intelligent control system framework: AIAA 1992-3162[R]. Reston: AIAA, 1992. |
52 | LORENZO C F, Ray A, HOLMES M S, Nonlinear control of a reusable rocket engine for life extension[J], Journal of Propulsion and Power, 2001, 17(5): 998-1004. |
53 | Carl F L and JEFFREY L M. Overview of rocket engine control: NASA-TM-105318 [R].Cleveland: NASA Lewis Research Center, 1991. |
54 | DAI X W, RAY A. Damage-mitigating control of a reusable rocket engine: Part Ⅱ-formulation of an optimal policy[J]. Journal of Dynamic Systems, Measurement, and Control, 1996, 118(3): 409-415. |
55 | MERRILL W, LORENZO C. A reusable rocket engine intelligent control: AIAA-1988-3114[R]. Reston: AIAA, 1988. |
56 | GONCHAROV N, ORLOV V, RACHUK V, et al. Reusable launch vehicle propulsion based on the RD-0120 engine: AIAA-1995-3003[R]. Reston: AIAA, 1995. |
57 | RUDIS M, ORLOV V, RACHUCK V, et al. A universal methodology for predicting liquid rocket engine durability based on Russian RD-0120 engine operating experience: AIAA-1995-2963[R]. Reston: AIAA, 1995. |
58 | Биргер Л А, Шорр Б Ф, Демянушко И В, др. Подробности тепловой прочности машины.[M] Москва: Машиностроение, 1975. |
BIRGER L A, SHORR B F, DEMYANUSHKO I V, et al. Details of the thermal strength of the machine[M] Moscow: Mechanical Manufacturing, 1975 (in Russian). | |
59 | FUKUSHIMA Y, LMOTO T. Lessons learned in the development of the LE-5 and LE-7: AIAA-1994-3375[R]. Reston: AIAA, 1994. |
60 | KISHIMOTO K, KAKUMA Y, KOYARI Y. LE-5 engine start sequence and its extension to the expander-bleed-cycle: AIAA-1991-2567[R]. Reston: AIAA, 1991. |
61 | FUJITA M, FUKUSHIMA Y. Improvement of LE-5A and LE-7 engine: AIAA-1996-2847[R]. Reston: AIAA, 1996. |
62 | KAMIJO K, YAMADA H, SAKAZUME N, et al. Developmental history of liquid oxygen turbopumps for the LE-7 engine[J]. Transactions of the Japan Society for Aeronautical and Space Sciences, 2001, 44(145): 155-163. |
63 | NAKANISHI H, SOGAME E, SUZUKI A, et al. Le-5 oxygen-hydrogen rocket engine for h-i launch vehicle[M]∥Space Mankind's Fourth Environment. Amsterdam: Elsevier, 1982: 511-522. |
64 | KANMURIi A, KAMDA T, WAKAMATSU T. Analysis of LOX/LH2 rocket engine (LE-7)[J]. AIAA Journal, 1989(89):2736 |
65 | SUNAKAWA H, KOBAYASHI T, OKITA K, et al. Development status of electrical valve control system for LE-9 engine: AIAA-2017-4671[R]. Reston: AIAA, 2017. |
66 | NEGORO N, TAMURA T, MANAKO H, et al. Overview of LE-9 engine development for H3 launch vehicle: IAC-16-C4.1.2[R]. Paris: International Astronautical Federation, 2016. |
67 | DE KORVER V, LASSOUDIERE F, FIORENTINO C, et al. Vulcain X technological demonstration roadmap: AIAA-2007-5489[R]. Reston: AIAA, 2007. |
68 | ALLIOT P, LASSOUDIERE F, FIORENTINO C, et al. Develoment status of the vinci engine for the ARIANE 5 upper stage: AIAA-2005-3755[R]. Reston: AIAA, 2005. |
69 | GOIRAND B, GALLARDO J F, BOSSON R. Vinci hydrogen turbopump-A new step in safe, faster and cheaper developments: AIAA-2000-3156[R]. Reston: AIAA, 2000. |
70 | 李斌, 张小平, 马冬英. 我国新一代载人火箭液氧煤油发动机[J]. 载人航天, 2014, 20(5): 427-431. |
LI B, ZHANG X P, MA D Y. The LOX/Kerosene rocket engine for Chinese new-generation manned launch vechicle. J]. Manned Spaceflight, 2014, 20(5): 427-431 (in Chinese). | |
71 | MUSGRAVE J, PAXSON D, LITT J, et al. A demonstration of an intelligent control system for a reusable rocket engine: NASA-TM-105794[R]. Cleveland: NASA Lewis Research Center, 1992. |
72 | LITT J S, SIMON D L, GARG S, et al. A survey of intelligent control and health management technologies for aircraft propulsion systems[J]. Journal of Aerospace Computing, Information, and Communication, 2004, 1(12): 543-563. |
73 | MUSGRAVE J L, PAXSON D E and LITT J S. A demonstration of an intelligent control system for a reusable rocket engine: NASA-TM-105794[R]. Cleveland: NASA Lewis Research Center, 1992. |
74 | 包为民, 祁振强, 张玉. 智能控制技术发展的思考[J]. 中国科学(信息科学), 2020, 50(8): 1267-1272. |
BAO W M, QI Z Q, ZHANG Y. Thoughts on the development of intelligent control technology[J]. Scientia Sinica (Informationis), 2020, 50(8): 1267-1272 (in Chinese). | |
75 | PéREZ-ROCA S, MARZAT J, PIET-LAHANIER H, et al. A survey of automatic control methods for liquid-propellant rocket engines[J]. Progress in Aerospace Sciences, 2019, 107: 63-84. |
76 | WAXENEGGER-WILFING G, DRESIA K, DEEKEN J, et al. A reinforcement learning approach for transient control of liquid rocket engines[J]. IEEE Transactions on Aerospace and Electronic Systems, 2021, 57(5): 2938-2952. |
77 | CHENG L, WANG Z B, JIANG F H. Real-time control for fuel-optimal Moon landing based on an interactive deep reinforcement learning algorithm[J]. Astrodynamics, 2019, 3(4): 375-386. |
78 | DRESIA K, JENTZSCH S, WAXENEGGER-WILFING G, et al. Multidisciplinary design optimization of reusable launch vehicles for different propellants and objectives[J]. Journal of Spacecraft and Rockets, 2021, 58(4): 1017-1029. |
79 | MARIOLIS I, PELEKA G, KARGAKOS A, et al. Pose and category recognition of highly deformable objects using deep learning[C]∥ 2015 International Conference on Advanced Robotics (ICAR). Piscataway: IEEE Press, 2015: 655-662. |
80 | KAPPLER D, BOHG J, SCHAAL S. Leveraging big data for grasp planning[C]∥ 2015 IEEE International Conference on Robotics and Automation (ICRA). Piscataway: IEEE Press, 2015: 4304-4311. |
81 | PIERSON H A, GASHLER M S. Deep learning in robotics: A review of recent research[J]. Advanced Robotics, 2017, 31(16): 821-835. |
82 | NEVEROVA N, WOLF C, TAYLOR G W, et al. Multi-scale deep learning for gesture detection and localization[C]∥European Conference on Computer Vision. Cham: Springer Cham, 2015: 474-490. |
83 | POLYDOROS A S, NALPANTIDIS L, KRüGER V. Real-time deep learning of robotic manipulator inverse dynamics[C]∥ 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Piscataway: IEEE Press, 2015: 3442-3448. |
84 | ZHANG T H, KAHN G, LEVINE S, et al. Learning deep control policies for autonomous aerial vehicles with MPC-guided policy search[C]∥ 2016 IEEE International Conference on Robotics and Automation (ICRA). Piscataway: IEEE Press, 2016: 528-535. |
85 | RAY A, HOLMES M S, LORENZO C F. Life extending controller design for reusable rocket engines[J]. The Aeronautical Journal, 2001, 105(1048): 315-322. |
86 | 吴建军, 魏鹏飞. 可重复使用液体火箭发动机智能减损控制技术[J]. 火箭推进, 2005, 31(4)8-14. |
WU J J, WEI P F. Intelligent damage-mitigating control techniques for reusable liquid-propellant rocket engines[J]. Journal of Rocket Propulsion, 2005, 31(4):8-14 (in Chinese). | |
87 | 程玉强, 吴建军, 刘洪刚. 基于粒子群算法的可重复使用液体火箭发动机减损优化问题研究[J]. 弹箭与制导学报, 2009, 29(2): 151-154. |
CHENG Y Q, WU J J, LIU H. Particle swarm optimization algorithm based damage-mitigating control for reusable liquid propellant rocket engine[J]. Journal of Projectiles, Rockets, Missiles and Guidance, 2009, 29(2): 151-154 (in Chinese). | |
88 | LE G S. Automatic & Control applications in the European space propulsion domain. From need expression to preparation for an uncertain future[C]∥ACD2016 Airbus Safran Launchers. Toulous:Airbus, 2016. |
89 | CARL F L, WALTER C M. Life extending control: NASA-92A29166[R]. Washington, D. C. : NASA, 1991. |
90 | WANG L, LIU Y P, LIU X D. A novel global sliding mode control strategy for attitude control of reusable launch vehicles in the reentry phase[C]∥ 2017 36th Chinese Control Conference (CCC). Piscataway: IEEE Press, 2017: 3615-3620. |
91 | 都延丽, 林海兵, 刘武, 等. 可重复使用运载器预测制导与鲁棒容错控制[J]. 哈尔滨工业大学学报, 2021, 53(6):62-70. |
DU Y L, LIN H B, LIU W, et al. Predictive guidance and robust fault-tolerant control for RLVs[J]. Journal of Harbin Institute of Technology, 2021, 53(6): 62-70 (in Chinese). | |
92 | XU K J, GUO Y Q, LIU L Y. Multivariable control coupling analysis and ALQR thrust controller design of liquid rocket engine[C]∥ 2021 12th International Conference on Mechanical and Aerospace Engineering (ICMAE). Piscataway: IEEE Press, 2021: 187-192. |
93 | 王军杰, 钟明磊. Meilin和猛禽液体火箭发动机技术研究与启示[C]∥第五届空天动力联合会议暨中国航天第三专业信息网第41届技术交流会论文集(第四册). 北京: 中国学术期刊电子出版社, 2020: 115-122. |
WANG J J, ZHONG M L. Meilin and Raptor liquid rocket engine technology research and enlightenment [C]∥Proceedings of the 5th Aerospace Power Joint Conference and the 41st Technical Exchange Meeting of China Aerospace Third Professional Information Network (Volume 4). Beijing: China Academic Journal Electrotic publishing House, 2020: 115-122 (in Chinese). | |
94 | BERGER B. Falcon 1 Failure Traced to a Busted Nut [EB/OL]. Space.com. (2006-07-19) [2023-01-13]. . |
95 | WHITESIDES L H. SpaceX Completes Development of Rocket Engine for Falcon 1 and 9[EB/OL]. Wired Science. (2008-3-23) [2023-01-13] . |
96 | GASKILL B. SpaceX has magical goals for Falcon 9[EB/OL]. Nasa Spaceflight. (2008-02-28) [2023-01-13]. . |
97 | NELSON K. SpaceX CRS-1 mission update[EB/OL]. (2016-3-31) [2023-01-13]. . |
98 | SpaceX. Archived from the original on March 7[EB/OL] (2020-9-4) [2023-01-13]. ∥. |
99 | Berger, Eric. How a tiny bit of lacquer grounded new Falcon 9 rockets for a month [EB/OL] (2020-10-29) [2023-01-13]. . |
100 | SpaceX. Falcon 9 [EB/OL]. (2018-2-8) [2023-01-13]. ∥. |
101 | Falcon SpaceX 9 Data Sheet [EB/OL]. Space Launch Report. (2019-9-21) [2023-01-13]. ∥. |
102 | Evolution of the SapceX Merlin-1 engine (and parameter) [EB/OL] (2022-2-12) [2023-01-13]. . |
103 | TODD D. Musk goes for methane-burning reusable rockets as step to colonise Mars[EB/OL]. (2012-11-20) [2023-01-13]. . |
104 | TODD D. SpaceX's Mars rocket to be methane-fuelled[EB/OL]. Flightglobal. (2012-11-22) [2023-01-13]. . |
105 | NASA Stennis Space Center to Test SpaceX Next Generation Rocket Engines Systems [EB/OL]. Mississippi development authority. (2013-10-2) [2023-01-13]. . |
106 | ELLUSCIO A G. ITS Propulsion – The evolution of the SpaceX Raptor engine[EB/OL]. NASASpaceFlight.com. (2021-126) [2023-01-13]. . |
107 | Ship 20 prepares for Static Fire - New Raptor 2 factory rises[EB/OL]. NASASpaceFlight.com. (2021-10-11) [2023-01-13]. . |
108 | Musk Elon. 250 tons of force is achievable [EB/OL]. Twitter. (2022-6-12) [2023-01-13] . |
109 | TREVOR S. Raptor 1 vs Raptor 2: What did spaceX change? [EB/OL]. (2022-7-14) [2023-01-13]. . |
110 | A SpaceX technician was in a coma for two months after a rocket test accident: Elon Musk’s company, silent [EB/OL] (2022-10-20) [2023-01-13]. . |
111 | URNES J, YEAGER R. Flight demonstration of the self-repairing flight control system in a NASA F-15 aircraft: AIAA-1991-3106[R]. Reston: AIAA, 1991. |
112 | BAUMGARTEN G, BUCHHOLZ J, HEINE W. A new reconfiguration concept for flight control systems[C]∥ Guidance, Navigation, and Control Conference. Reston: AIAA, 1995: 9-19. |
113 | HODEL A, CALLAHAN R. Autonomous reconfigurable control allocation (ARCA) for reusable launch vehicles: AIAA-2002-4777[R]. Reston: AIAA, 2002. |
114 | SHAFFER P J, ROSS I M, OPPENHEIMER M W, et al. Optimal trajectory reconfiguration and retargeting for reusable launch vehicles[J]. Journal of Guidance, Control, and Dynamics, 2007, 30(6): 1794-1802. |
115 | YIKILMAZ C, üRE N K. Deep learning based fault tolerant thrust vector control: AIAA-2022-0970[R]. Reston: AIAA, 2022. |
116 | 周川, 胡维礼, 陈庆伟, 等. 智能重构控制技术及其应用[J]. 系统工程与电子技术, 2001, 23(6): 59-62. |
ZHOU C, HU W L, CHEN Q W, et al. Method and application of intelligent reconfigurable control[J]. Systems Engineering and Electronics, 2001, 23(6): 59-62 (in Chinese). | |
117 | 朱海洋, 吴燕生, 容易, 等. 适应有限故障的运载火箭神经网络自适应容错控制[J]. 西北工业大学学报, 2020, 38(3): 668-676. |
ZHU H Y, WU Y S, RONG Y, et al. A neural network adaptive fault-tolerant control method for launch vehicles with the limited faults[J]. Journal of Northwestern Polytechnical University, 2020, 38(3): 668-676 (in Chinese). | |
118 | 马艳如, 石晓荣, 刘华华, 等. 运载火箭姿态系统自适应神经网络容错控制[J]. 宇航学报, 2021, 42(10): 1237-1245. |
MA Y R, SHI X R, LIU H H, et al. Adaptive neural network fault tolerant control of launch vehicle attitude system[J]. Journal of Astronautics, 2021, 42(10): 1237-1245 (in Chinese). | |
119 | 李爽, 刘旭, 叶松, 等. 运载火箭动力系统故障下制导控制技术研究进展[J]. 上海航天(中英文), 2022(4): 76-93. |
LI S, LIU X, YE S, et al. Overview of guidance and control technologies for launch vehicles under propulsion system faults[J]. Aerospace Shanghai (Chinese&English), 2022(4): 76-93 (in Chinese). | |
120 | 吴建军, 朱晓彬, 程玉强, 等. 液体火箭发动机智能健康监控技术研究进展[J]. 推进技术, 2022, 43(1): 1-13. |
WU J J, ZHU X B, CHENG Y Q, et al. Research progress of intelligent health monitoring technology for liquid-propellant rocket engines[J]. Journal of Propulsion Technology, 2022, 43(1): 1-13 (in Chinese). | |
121 | PETTIT C, BARKHOUDARIAN S, DAUMANN J A, et al. Reusable rocket engine advanced health management system: Architecture and technology evaluation-Summary: AIAA-1999-2527[R]. Reston: AIAA, 1999. |
122 | WU G. A fuzzy logic intelligent diagnostic system for spacecraft integrated vehicle health management: N95-27392[R]. Washington, D.C.: NASA, 1995. |
123 | FIGUEROA J, MELCHER K. Integrated systems health management for intelligent systems: AIAA-2011-1492[R]. Reston: AIAA, 2011. |
124 | 袁利, 王淑一. 航天器控制系统智能健康管理技术发展综述[J]. 航空学报, 2021, 42(4): 525044. |
YUAN L, WANG S Y. A review on development of intelligent health management technology for spacecraft control systems[J]. Acta Aeronautica et Astronautica Sinica, 2021, 42(4): 525044 (in Chinese). | |
125 | 王昊义, 吴建军, 张宇, 等. 基于吸气式电推进的智能姿轨控系统: CN112572833A[P]. 2021-03-30. |
WANG H Y, WU J J, ZHANG Y, et al. Intelligent attitude and orbit control system based on air-breathing electric propulsion: CN112572833A[P]. 2021-03-30 (in Chinese). | |
126 | 吴建军,郑鹏,张宇 等,一种全电推进立方体卫星 : CN202111595136.8[P]. 2022-03-04. |
WU J J, ZHENG P, ZHANHG Y, et al. An all electric propulsion cube satellite: CN202111595136.8[P]. 2022-03-04 (in Chinese). | |
127 | 吴必琦, 吴建军, 程玉强, 等. 可应用于多流态的智能控制吸气式电推进系统: CN110748467B[P]. 2020-08-21. |
吴必琦, 吴建军, 程玉强, 张宇, 李健, 谭胜, 欧阳, 杜忻洳. Intelligent control air suction type electric propulsion system applicable to multi-flow state: CN110748467B[P]. 2020-08-21 (in Chinese). | |
128 | 朱炳杰, 杨希祥, 宗建安, 等. 分布式混合电推进飞行器技术[J]. 航空学报, 2022, 43(7): 025556. |
ZHU B J, YANG X X, ZONG J A, et al. Review of distributed hybrid electric propulsion aircraft technology[J]. Acta Aeronautica et Astronautica Sinica, 2022, 43(7): 025556 (in Chinese). | |
129 | 韩明仁, 王玉峰. 基于强化学习的全电推进卫星变轨优化方法[J]. 系统工程与电子技术, 2022, 44(5): 1652-1661. |
HAN M R, WANG Y F. Optimization method for orbit transfer of all-electric propulsion satellite based on reinforcement learning[J]. Systems Engineering and Electronics, 2022, 44(5): 1652-1661 (in Chinese). | |
130 | 张远, 黄旭, 路坤锋, 等. 高超声速飞行器控制技术研究进展与展望[J]. 宇航学报, 2022, 43(7): 866-879. |
ZHANG Y, HUANG X, LU K F, et al. Research progress and prospect of the hypersonic flight vehicle control technology[J]. Journal of Astronautics, 2022, 43(7): 866-879 (in Chinese). | |
131 | 常亚菲, 姜甜甜. 高超声速再入飞行器的特征建模及自适应递推滑模控制[J]. 宇航学报, 2018, 39(8): 889-899. |
CHANG Y F, JIANG T T. Characteristic modeling and adaptive recursive sliding mode control for hypersonic reentry vehicle[J]. Journal of Astronautics, 2018, 39(8): 889-899 (in Chinese). | |
132 | 王冠, 马长波, 茹海忠, 等. 一种非仿射高超声速飞行器的智能控制方法[J]. 飞控与探测, 2021(4): 59-65. |
WANG G, MA C B, RU H Z, et al. An intelligent control method for non-affine hypersonic vehicles[J]. Flight Control & Detection, 2021(4): 59-65 (in Chinese). | |
133 | 田维平, 雷晓龙, 唐敏, 等. 固体动力智能化发展技术展望[J]. 固体火箭技术, 2021, 44(2): 146-150. |
TIAN W P, LEI X L, TANG M, et al. Prospect of the intelligentized solid propulsion technology[J]. Journal of Solid Rocket Technology, 2021, 44(2): 146-150 (in Chinese). | |
134 | 孙明波, 安彬, 汪洪波, 等. 超燃冲压发动机仿真: 从数值飞行到数智飞行[J]. 力学学报, 2022, 54(3): 588-600. |
SUN M B, AN B, WANG H B, et al. Numerical simulation of the scramjet engine: From numerical flight to intelligent numerical flight[J]. Chinese Journal of Theoretical and Applied Mechanics, 2022, 54(3): 588-600 (in Chinese). | |
135 | 吴宏鑫, 谈树萍. 航天器控制的现状与未来[J]. 空间控制技术与应用, 2012, 38(5): 1-7. |
WU H X, TAN S P. Spacecraft control: present and future[J]. Aerospace Control and Application, 2012, 38(5): 1-7 (in Chinese). | |
136 | 吴宏鑫, 胡军, 解永春. 航天器智能自主控制研究的回顾与展望[J]. 空间控制技术与应用, 2016, 42(1): 1-6. |
WU H X, HU J, XIE Y C. Spacecraft intelligent autonomous control: Past, present and future[J]. Aerospace Control and Application, 2016, 42(1): 1-6 (in Chinese). | |
137 | KLEIN M, HAYOUN D, GONIDEC S L, et al. Method and a circuit for regulating a rocket engine: US20170101963A1[P]. 2017. |
138 | GONIDEC S L. Device for controlling a regulated system, and an engine including such a device: US8005554[P]. 2011. |
139 | GONIDEC L E. Method for controlling the pressure and a mixture ratio of a rocket engine, and corresponding device: EP3332108A1[P]. 2015. |
140 | 张守武, 王恒, 陈鹏, 等. 神经网络在无人驾驶车辆运动控制中的应用综述[J]. 工程科学学报, 2022, 44(2): 235-243. |
ZHANG S W, WANG H, CHEN P, et al. Overview of the application of neural networks in the motion control of unmanned vehicles[J]. Chinese Journal of Engineering, 2022, 44(2): 235-243 (in Chinese). | |
141 | 程林, 蒋方华, 李俊峰. 深度学习在飞行器动力学与控制中的应用研究综述[J]. 力学与实践, 2020, 42(3): 267-276. |
CHENG L, JIANG F H, LI J F. A review on the applications of deep learning in aircraft dynamics and control[J]. Mechanics in Engineering, 2020, 42(3): 267-276 (in Chinese). | |
142 | XU X, HU D W, HE H G. Accelerated reinforcement learning control using modified CMAC neural networks[C]∥ Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02. Piscataway: IEEE Press, 2003: 2575-2578. |
143 | XU X, LIAN C Q, ZUO L, et al. Kernel-based approximate dynamic programming for real-time online learning control: An experimental study[J]. IEEE Transactions on Control Systems Technology, 2014, 22(1): 146-156. |
144 | LU Y, XU X, ZHANG X L, et al. Hierarchical reinforcement learning for autonomous decision making and motion planning of intelligent vehicles[J]. IEEE Access, 2020, 8: 209776-209789. |
145 | ZHANG X L, PENG Y Q, LUO B, et al. Model-based safe reinforcement learning with time-varying state and control constraints: An application to intelligent vehicles [J]. Journal of Latex Class Filtes, 2021, 14(8) :1-14. |
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