[1]YANG Jingyu,GAO Liang,LI Di,et al.Advancements in the key technologies of intelligent cooperative control of co-location monitoring satellites[J].CAAI Transactions on Intelligent Systems,2022,17(6):1063-1073.[doi:10.11992/tis.202107050]
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Advancements in the key technologies of intelligent cooperative control of co-location monitoring satellites
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
17
Number of periods:
2022 6
Page number:
1063-1073
Column:
综述
Public date:
2022-11-05
- Title:
- Advancements in the key technologies of intelligent cooperative control of co-location monitoring satellites
- Keywords:
- co-location monitoring satellite; cooperative control; artificial intelligence; satellite formation; intelligent algorithm; onboard system; intelligentce; on-orbit training
- CLC:
- TP18;V423.4
- DOI:
- 10.11992/tis.202107050
- Abstract:
- With the development of deep space exploration and spacecraft intelligence, intelligent cooperative control on an orbit will be a key future technology. First, this study discusses the current technology development status and the application of intelligent cooperative control on the monitoring satellite platform in terms of the hardware design of the satellite onboard system. Second, the development requirements of future satellite systems are discussed, focusing on the current situation of intelligent satellite systems and the basic characteristics of future satellite collaboration. Next, the problem of cooperative control of intelligent co-located cooperative formation satellite swarms and the key technical implementation routes required for in-orbit monitoring are discussed and summarized. Finally, an in-depth systematic analysis is conducted for the intelligent cooperative algorithm for satellite formation, highlighting the feasibility and good prospects of the AI technology’s application in future co-located formation tasks.
- References:
-
[1] ASTRAIN M, RUIZ M, CARPE?O A, et al. Development of deep learning applications in FPGA-based fusion diagnostics using IRIO-OpenCL and NDS[J]. Fusion engineering and design, 2021, 168: 112393.
[2] BLAIECH A G, BEN KHALIFA K, VALDERRAMA C, et al. A survey and taxonomy of FPGA-based deep learning accelerators[J]. Journal of systems architecture, 2019, 98: 331–345.
[3] 余成宇, 李志远, 毛文宇, 等. 一种高效的稀疏卷积神经网络加速器的设计与实现[J]. 智能系统学报, 2019, 98(2): 331–345
YU Chengyu, LI Zhiyuan, MAO Wenyu, et al. Design and implementation of an efficient accelerator for sparse convolutional neural network[J]. CAAI transactions on intelligent systems, 2019, 98(2): 331–345
[4] SHI Keke, LIU Chuang, BIGGS J D, et al. Observer-based control for spacecraft electromagnetic docking[J]. Aerospace science and technology, 2020, 99: 105759.
[5] HU Qinglei, SHI Yongxia, SHAO Xiaodong. Adaptive fault-tolerant attitude control for satellite reorientation under input saturation[J]. Aerospace science and technology, 2018, 78: 171–182.
[6] XI Changjiang, DONG Jiuxiang. Adaptive neural network-based control of uncertain nonlinear systems with time-varying full-state constraints and input constraint[J]. Neurocomputing, 2019, 357: 108–115.
[7] GUO Jianlan, CHEN Yuqiang, LAI Guanyu, et al. Neural networks-based adaptive control of uncertain nonlinear systems with unknown input constraints[J]. Journal of ambient intelligence and humanized computing, 2021, 3(1): 1–13.
[8] WANG Zhiqiang, YANG Mingbo, GAO Le, et al. Deadbeat predictive current control of permanent magnet synchronous motor based on variable step-size adaline neural network parameter identification[J]. IET electric power applications, 2020, 14(11): 2007–2015.
[9] WANG Yaoyao, YAN Fei, JIANG Surong, et al. Adaptive nonsingular terminal sliding mode control of cable-driven manipulators with time delay estimation[J]. International journal of systems science, 2020, 51(1): 1–19.
[10] LEE C, AN D. Reinforcement learning and neural network-based artificial intelligence control algorithm for self-balancing quadruped robot[J]. Journal of mechanical science and technology, 2021, 35(1): 307–322.
[11] SAMAD T, BAUER M, BORTOFF S, et al. Industry engagement with control research: perspective and messages[J]. Annual reviews in control, 2020, 49: 1–14.
[12] PLATT C A, JASON M, SULLIVAN C J. Public perceptions of private space initiatives: how young adults view the SpaceX plan to colonize Mars[J]. Space policy, 2020, 51: 101358.
[13] REILAND N, ROSENGREN A J, MALHOTRA R, et al. Assessing and minimizing collisions in satellite mega-constellations[J]. Advances in space research, 2021, 67(11): 3755–3774.
[14] YANG Zhengquan, PAN Xiaofang, ZHANG Qing, et al. Finite-time formation control for first-order multi-agent systems with region constraints[J]. Frontiers of information technology & electronic engineering, 2021, 22(1): 134–140.
[15] WANG Xiao, SHI Peng, WEN Changxuan, et al. An algorithm of reinforcement learning for maneuvering parameter self-tuning applying in satellite cluster[J]. Mathematical problems in engineering, 2020, 2020(5): 1–17.
[16] XU Chuang, WU Baolin, ZHANG Yingchun. Distributed prescribed-time attitude cooperative control for multiple spacecraft[J]. Aerospace science and technology, 2021, 113: 106699.
[17] HUANG Hailong, SAVKIN A V. Energy-efficient decentralized navigation of a team of solar-powered UAVs for collaborative eavesdropping on a mobile ground target in urban environments[J]. Ad hoc networks, 2021, 117: 102485.
[18] FENG Zhi, HU Guoqiang, SUN Yajuan, et al. An overview of collaborative robotic manipulation in multi-robot systems[J]. Annual reviews in control, 2020, 49: 113–127.
[19] MOHAJER A, BAVAGHAR M, FARROKHI H. Mobility-aware load balancing for reliable self-organization networks: multi-agent deep reinforcement learning[J]. Reliability engineering & system safety, 2020, 202: 107056.
[20] KANG Jingu, LIM D W, CHOI Y S, et al. Improved RRT-connect algorithm based on triangular inequality for robot path planning[J]. Sensors (Basel, Switzerland), 2021, 21(2): 333.
[21] CHAI Runqi, TSOURDOS A, SAVVARIS A, et al. Review of advanced guidance and control algorithms for space/aerospace vehicles[J]. Progress in aerospace sciences, 2021, 122: 100696.
[22] ACEVEDO J J, MAZA I, OLLERO A, et al. An efficient distributed area division method for cooperative monitoring applications with multiple UAVs[J]. Sensors (Basel, Switzerland), 2020, 20(12): 3448.
[23] ANDRADE E, SANTOS A, MACIEL P D, et al. Analyzing cooperative monitoring and dissemination of critical mobile events by VANETs[J]. Wireless networks, 2021, 27(3): 1981–1997.
[24] ZHOU Liang, LUO Jianjun, NOGUEIRA T, et al. Orbit design and control method for satellite clusters and its applications to NetSat project[J]. Proceedings of the institution of mechanical engineers, part G:journal of aerospace engineering, 2018, 232(8): 1559–1570.
[25] FAN Liming, HUANG Hai. Coordinative coupled attitude and orbit control for satellite formation with multiple uncertainties and actuator saturation[J]. Acta astronautica, 2021, 181: 325–335.
[26] 姚文刚, 张蕾蕾, 周宾, 等. 箭载飞行控制计算机的国产化设计[J]. 导弹与航天运载技术, 2019(2): 54–57
YAO Wengang, ZHANG Leilei, ZHOU Bin, et al. Domestic design of rocket computer[J]. Missiles and space vehicles, 2019(2): 54–57
[27] TAO Jin, SUN Qinglin, CHEN Zengqiang, et al. NSGAII based multi-objective homing trajectory planning of parafoil system[J]. Journal of central south university, 2016, 23(12): 3248–3255.
[28] 柳强, 焦国帅. 基于Kriging模型和NSGA-Ⅱ的航空发动机管路卡箍布局优化[J]. 智能系统学报, 2019, 14(2): 281–287
LIU Qiang, JIAO Guoshuai. Layout optimization of aero-engine pipe clamps based on Kriging model and NSGA-II[J]. CAAI transactions on intelligent systems, 2019, 14(2): 281–287
[29] HOBBIE J G, GANDOMI A H, RAHIMI I. A comparison of constraint handling techniques on NSGA-II[J]. Archives of computational methods in engineering, 2021, 28(5): 3475–3490.
[30] HAN Yi, WANG Lei, FU Wenju, et al. LEO navigation augmentation constellation design with the multi-objective optimization approaches[J]. Chinese journal of aeronautics, 2021, 34(4): 265–278.
[31] RAJANI, KUMAR D, KUMAR V. Impact of controlling parameters on the performance of MOPSO algorithm[J]. Procedia computer science, 2020, 167: 2132–2139.
[32] JIANG Xiuqiang, LI Shuang, FURFARO R. Integrated guidance for Mars entry and powered descent using reinforcement learning and pseudospectral method[J]. Acta astronautica, 2019, 163: 114–129.
[33] MA Zhong, WANG Yuejiao, YANG Yidai, et al. Reinforcement learning-based satellite attitude stabilization method for non-cooperative target capturing[J]. Sensors, 2018, 18(12): 4331.
[34] WU Xiande, ZHAO Han, HUANG Bing, et al. Minimum-learning-parameter-based anti-unwinding attitude tracking control for spacecraft with unknown inertia parameters[J]. Acta astronautica, 2021, 179: 498–508.
[35] EBEL H, EBERHARD P. Optimization-driven control and organization of a robot swarm for cooperative transportation[J]. IFAC-PapersOnLine, 2019, 52(15): 115–120.
[36] FREY G R, PETERSEN C D, LEVE F A, et al. Time shift governor for coordinated control of two spacecraft formations[J]. IFAC-PapersOnLine, 2016, 49(18): 296–301.
[37] SHOJA S, BARADARANNIA M, HASHEMZADEH F, et al. Surrounding control of nonlinear multi-agent systems with non-identical agents[J]. ISA transactions, 2017, 70: 219–227.
[38] SUN Hao, WANG Fuyong, SUN Qinglin, et al. Distributed consensus algorithm for multiple parafoils in mass airdrop mission based on disturbance rejection[J]. Aerospace science and technology, 2021, 109: 106437.
[39] 潘无为. 分布式多水下机器人编队控制方法研究[D]. 哈尔滨: 哈尔滨工程大学, 2018.
PAN Wuwei. Study on distributed formation control of multiple autonomous underwater vehicles[D]. Harbin: Harbin Engineering University, 2018.
[40] 丛凯, 张金春, 卢翰. 部分具有引导信息的多领导者蜂拥控制算法[J]. 舰船电子工程, 2019, 39(4): 22–28, 116
CONG Kai, ZHANG Jinchun, LU Han. Multi-leader flocking control algorithm with partly information[J]. Ship electronic engineering, 2019, 39(4): 22–28, 116
[41] 许轲, 吴凤鸽, 赵军锁. 基于深度强化学习的软件定义卫星姿态控制算法[J]. 北京航空航天大学学报, 2018, 44(12): 2651–2659
XU Ke, WU Fengge, ZHAO Junsuo. Software defined satellite attitude control algorithm Based on deep reinforcement learning[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(12): 2651–2659
[42] LIU Haitao, CHEN Guangjun, TIAN Xuehong. Cooperative formation control for multiple surface vessels based on barrier Lyapunov function and self-structuring neural networks[J]. Ocean engineering, 2020, 216: 108163.
[43] DEHGHANI M A, MENHAJ M B. Communication free leader-follower formation control of unmanned aircraft systems[J]. Robotics and autonomous systems, 2016, 80: 69–75.
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1900-01-01