Analysis of Autonomous Aerial Refueling Capability Requirements and Key Evaluation Indicators

doi:10.16182/j.issn1004731x.joss.22-0655

Abstract

Abstract:

From the perspective of flight tests, how to evaluate the autonomous aerial refueling (AAR) capability and select key indicators for evaluation is a key problem to be solved for AAR trials. The standards requirements of aerial refueling and manned aircraft aerial refueling experience in China and abroad are analyzed. The total capability of AAR is studied, and key evaluation indicators in the AAR whole process including rendezvous, formation, docking, refueling, and disengagement are proposed. The evaluation method is demonstrated in both numerical simulation and hardware-in-loop test environments. Finally, the key indicators affecting the docking success of AAR are analyzed, and suggestions for evaluation indicators are proposed.The test results can provide a reference for the process design and flight test verification of AAR.

Key words: autonomous aerial refueling(AAR), capability requirements, docking process, evaluation indicators, hardware-in-loop(HIL)

CLC Number:

TP391.9

Zou Quan, Hua Yixin, Shao Zhu, Zhao Wenbi. Analysis of Autonomous Aerial Refueling Capability Requirements and Key Evaluation Indicators[J]. Journal of System Simulation, 2023, 35(11): 2385-2396.

Figures/Tables 14

Fig. 1

Table 1

Fig. 2

Table 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Fig. 9

Fig. 10

Table 3

Table 4

References 25

1	廖南杰. MQ-25无人机完成首次空中供油试验[N]. 中国航空报, 2021-07-06(A09).
2	钟德星, 李永强, 李严桵. 无人机自主空中加油技术现状及发展趋势[J]. 航空科学技术, 2014, 25(5): 1-6.
	Zhong Dexing, Li Yongqiang, Li Yanrui. State-of-the-art and Tendency of Autonomous Aerial Refueling Technologies for Unmanned Aerial Vehicles[J]. Aeronautical Science & Technology, 2014, 25(5): 1-6.
3	李明. 美国海军舰载无人空中加油系统项目及动力[J]. 航空动力, 2018(2): 40-44.
	Li Ming. U.S. Navy Carrier-based Unmanned Tanker MQ-25 and It's Engine Selection[J]. Aerospace Power, 2018(2): 40-44.
4	Dibley R P, Allen M J, Dr Nassib Nabaa. Autonomous Airborne Refueling Demonstration Phase I Flight-test Results[C]//AIAA Atmospheric Flight Mechanics Conference and Exhibit. Reston, VA, USA: AIAA, 2007: AIAA 2007-6639.
5	Ro K, Basaran E. Aerodynamic Investigations of Paradrogue Assembly in Aerial Refueling System[C]//44th AIAA Aerospace Sciences Meeting and Exhibit. Reston, VA, USA: AIAA, 2006: AIAA 2006-855.
6	全权, 魏子博, 高俊, 等. 软管式自主空中加油对接阶段中的建模与控制综述[J]. 航空学报, 2014, 35(9): 2390-2410.
	Quan Quan, Wei Zibo, Gao Jun, et al. A Survey on Modeling and Control Problems for Probe and Drogue Autonomous Aerial Refueling at Docking Stage[J]. Acta Aeronautica et Astronautica Sinica, 2014, 35(9): 2390-2410.
7	国防科学技术工业委员会. 插头-锥管式空中加油系统通用规范: [S]. 北京: 国防科学技术工业委员会, 1993.
	The Commission of Science, Technology and Industry for National Defense. In-flight Refuelling System, Type Probe and Drogue, General Specification for: [S]. Beijing: The Commission of Science, Technology and Industry for National Defense, 1993.
8	国防科学技术工业委员会. 插头-锥管式空中加油吊舱规范: [S]. 北京: 国防科学技术工业委员会, 1997.
	The Commission of Science, Technology and Industry for National Defense. Air to Air Refuelling (AAR) Pod Type of Probe and Drogue, Specification for: [S]. Beijing: The Commission of Science, Technology and Industry for National Defense, 1997.
9	国防科学技术工业委员会. 飞机燃油系统飞行试验要求: [S]. 北京: 国防科学技术工业委员会, 1998.
	The Commission of Science, Technology and Industry for National Defense. Flight Test Requirements for Aircraft Fuel System: [S]. Beijing: The Commission of Science, Technology and Industry for National Defense, 1998.
10	华艺欣, 邹泉, 田海铭. 软式自主空中加油控制策略仿真[J]. 北京航空航天大学学报, 2021, 47(2): 262-270.
	Hua Yixin, Zou Quan, Tian Haiming. Control Strategy and Simulation for Probe-and-drogue Aerial Autonomous Refueling[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(2): 262-270.
11	潘科, 潘宣宏, 郭新奇, 等. 空中加油机作战效能设计参数的主成分分析[J]. 舰船电子工程, 2017, 37(4): 21-24.
	Pan Ke, Pan Xuanhong, Guo Xinqi, et al. Principal Component Analysis on Design Parameters of Aerial Refueling Aircraft Operational Effectiveness[J]. Ship Electronic Engineering, 2017, 37(4): 21-24.
12	陈乐乐, 刘学强. 不同对接速度下软式加油管锥套运动特性数值模拟研究[J]. 空气动力学学报, 2017, 35(1): 115-122.
	Chen Lele, Liu Xueqiang. Dynamic Characteristics Analysis of Refueling Drogue at Various Docking Velocities[J]. Acta Aerodynamica Sinica, 2017, 35(1): 115-122.
13	陆宇平, 杨朝星, 刘洋洋. 空中加油系统的建模与控制技术综述[J]. 航空学报, 2014, 35(9): 2375-2389.
	Lu Yuping, Yang Chaoxing, Liu Yangyang. A Survey of Modeling and Control Technologies for Aerial Refueling System[J]. Acta Aeronautica et Astronautica Sinica, 2014, 35(9): 2375-2389.
14	王健, 董新民, 徐跃鉴, 等. 软式空中加油受油机头波数值仿真分析[J]. 飞行力学, 2016, 34(1): 54-58.
	Wang Jian, Dong Xinmin, Xu Yuejian, et al. Simulation and Analysis of the Bow Wave Effect of the Receiver in Hose-drogue Aerial Refueling[J]. Flight Dynamics, 2016, 34(1): 54-58.
15	王海涛, 董新民. 空中加油动力学与控制[M]. 北京: 国防工业出版社, 2016: 56-63.
16	徐坚, 张晓非. 软式空中加油头波效应建模与仿真[J]. 飞行力学, 2019, 37(5): 40-44.
	Xu Jian, Zhang Xiaofei. Dynamic Modeling and Simulation of Bow Wave Effect in Hose-drogue Aerial Refueling System[J]. Flight Dynamics, 2019, 37(5): 40-44.
17	谢磊, 孙绍山, 金智, 等. 无人自主空中加油相对引导技术[N]. 中国航空报, 2018-11-01(A05).
18	张博连. 无人机自主空中加油对接控制技术研究[D]. 哈尔滨: 哈尔滨工业大学, 2019.
	Zhang Bolian. Docking Control Method for Autonomous Aerial Refueling for Unmanned Aerial Vehicles[D]. Harbin: Harbin Institute of Technology, 2019.
19	陈娟, 赵君伟, 付永领, 等. 受油机运动模拟系统设计与建模仿真分析[J]. 机床与液压, 2017, 45(15): 10-14.
	Chen Juan, Zhao Junwei, Fu Yongling, et al. Design and Modeling Simulation Analysis of Motion Simulation System of Refueled Aircraft[J]. Machine Tool & Hydraulics, 2017, 45(15): 10-14.
20	秦勇, 王宏伦, 苏子康, 等. 基于视觉的自主空中加油锥套检测与跟踪[J]. 战术导弹技术, 2016(6): 2390-2410.
	Qin Yong, Wang Honglun, Su Zikang, et al. Drogue Detection and Tracking Based on Vision for Autonomous Aerial Refueling[J]. Tactical Missile Technology, 2016(6): 2390-2410.
21	李柱. 基于双目视觉的无人机自主空中加油对接导航方法[D]. 厦门: 厦门大学, 2018.
	Li Zhu. Binocular-vision-based Docking Navigation Method for UAV Self-refueling[D]. Xiamen: Xiamen University, 2018.
22	单尧. 空中加油对接系统视觉处理与飞行验证[D]. 南京: 南京航空航天大学, 2018.
	Shan Yao. Research on Detection Identification Algorithm of Drogue Images and Flight Verification[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2018.
23	周清, 许悦雷, 加尔肯别克. 无人机软管式自主空中加油视觉导航技术[J]. 导航定位与授时, 2020, 7(1): 40-47.
	Zhou Qing, Xu Yuelei, Jia Erkenbieke. Visual Navigation Technology for UAV Autonomous Hose-drogue Aerial Refueling[J]. Navigation Positioning and Timing, 2020, 7(1): 40-47.
24	解洪文, 王宏伦. 基于双目视觉的自动空中加油近距导航方法[J]. 北京航空航天大学学报, 2011, 37(2): 206-209.
	Xie Hongwen, Wang Honglun. Binocular Vision-based Short-range Navigation Method for Autonomous Aerial Refueling[J]. Journal of Beijing University of Aeronautics and Astronautics, 2011, 37(2): 206-209.
25	纪超, 王庆. 基于双目视觉的自主空中加油算法研究与仿真[J]. 系统仿真学报, 2013, 25(6): 1327-1331.
	Ji Chao, Wang Qing. Stereo Vision System and Simulation for Autonomous Aerial Refueling[J]. Journal of System Simulation, 2013, 25(6): 1327-1331.

评价项目	评价指标
总体能力	受油机编队飞机数量特定气象条件下的对接成功率
自主会合	会合时间到达会合控制点的控制精度
自主编队	编队位置保持精度编队效率
自主对接	加受油机相对位置测量精度相对对接速度对接保持控制精度对接保持时间
自主加油及脱离	加油编队位置保持精度脱离编队位置保持精度
其他	自主防撞能力等

初始位置		A	B	C	D
速度/ (km/h)	坡度/ (°)	计划会合时间/实际会合时间/s
396	20	334/357	318/325	283/307	408/425
432	20	345/367	324/351	269/282	400/437
396	30	335/353	311/319	272/298	375/411
432	30	321/342	300/312	257/269	364/390

紊流强度	相对速度/(m·s^-1)
紊流强度	0.6	0.9	1.2	1.5
轻度紊流	5/6	6/6	6/6	5/6
中度紊流	4/6	4/6	4/6	3/6

[1]	Chen Xue, Hu Rong, Wang Hui, Li Zuocheng, Qian Bin, Li Yixu. Learning-based Ant Colony Optimization Algorithm for Solving a Kind of Complex 2-Echelon Vehicle Routing Problem [J]. Journal of System Simulation, 2023, 35(11): 2476-2495.
[2]	Zhang Yingyu, Wu Liyun, Jia Shengtai. Multi-depot Half-open Vehicle Routing Problem with Simultaneous Delivery-pickup and Time Windows [J]. Journal of System Simulation, 2023, 35(11): 2464-2475.
[3]	Dong Zhiming, Si Bingshan, Li Liang. Requirements of Parallel Combat System Based on GQFD-Coupling Coordination Degree [J]. Journal of System Simulation, 2023, 35(11): 2454-2463.
[4]	Zhang Wenfeng, Zhu Zhichao, Wu Dinghui. Rolling Bearing Fault Diagnosis Based on Weighted Domain Adaptive Convolutional Neural Network [J]. Journal of System Simulation, 2023, 35(11): 2445-2453.
[5]	Jia Zhengxuan, Lin Tingyu, Xiao Yingying, Shi Guoqiang, Wang Hao, Zeng Bi, Ou Yiming, Zhao Pengpeng. Imitative Generation of Optimal Guidance Law Based on Reinforcement Learning [J]. Journal of System Simulation, 2023, 35(11): 2410-2418.
[6]	Wang Can, Ji Haoran, Guo Qisheng, Dong Zhiming, Tan Yaxin, Mu Ge. Development of Combat Concept of Intelligent Land Assault System Based on DoDAF [J]. Journal of System Simulation, 2023, 35(11): 2397-2409.
[7]	Zhang Tianrui, Liu Yuting, Wang Yike. Research on Multi-process Product Quality Prediction Based on Improved BiLSTM [J]. Journal of System Simulation, 2023, 35(11): 2321-2332.
[8]	Mao Ziquan, Gao Jialong, Gong Jianxing, Liu Quan. Application of Virtual-Real Simulation in Military Field [J]. Journal of System Simulation, 2023, 35(11): 2289-2311.
[9]	Li Dongsheng, Liu Ye, Song Yankan, Shen Chen. Electromagnetic Transient Equivalent Modeling Method for Wind Power Clusters Adapted to Expected Faults [J]. Journal of System Simulation, 2023, 35(10): 2101-2112.
[10]	Chen Shanshan, Wang Hongzhi, Xia Tian. Key Technology and Application of Digital Twin Modeling for MRI [J]. Journal of System Simulation, 2023, 35(10): 2122-2132.
[11]	Liu Lu, Li Wenxin, Song Xiao, Sun Bingli, Gong Guanghong. A Fuzzy Group Decision-making-based Method for Green Supplier Selection and Order Allocation [J]. Journal of System Simulation, 2023, 35(10): 2133-2149.
[12]	Wu Peng, Yang Zongmo, Jing Qianfeng, Li Yulin. A Hybrid Empirical Method for Fast Modeling of Ship Manoeuvring Motion [J]. Journal of System Simulation, 2023, 35(10): 2150-2160.
[13]	Zou Mengfan, He Xiaoyu. Modeling and Analysis on Scattering Characteristics Automatic Driving Radar Bands in Rainy Environment [J]. Journal of System Simulation, 2023, 35(10): 2161-2169.
[14]	Zhang Tianrui, Niu Huiyuan, Xie Wei. Integrated Scheduling Simulation Based on Improved Moth Flame Optimizer [J]. Journal of System Simulation, 2023, 35(10): 2170-2181.
[15]	Wen Rui. A Structured Conceptual Model of Joint Operations From Design Perspective [J]. Journal of System Simulation, 2023, 35(10): 2202-2211.