基于冲突点到达时间的航空器自主间隔控制律

doi:10.12305/j.issn.1001-506X.2024.10.25

Abstract

Abstract:

Transferring part of the responsibility for separation maintenance from the controller to the pilot during the route cruise phase can reduce the control load and improve the efficiency of airspace safe operation. For the new generation of distributed air traffic control mode, the own aircraft uses the target aircraft with potential conflict as the target aircraft for separation control based on the time-to-go of the intersection point, and builds an autonomous separation control model based on the specified separation values of time and space in the cross-conflict, respectively. Taking the calibrated airspeed of aircraft's maneuvering flight as the autonomous separation control strategy of the aircraft, the purpose of effectively controlling the separation between two aircrafts is achieved by controlling the calibrated airspeed of the own aircraft. Finally, a horizontal cross-conflict scenario is set up to conduct a simulation comparison experiment. Simulation results show the effectiveness and practicability of the separation control strategy in this paper for distributed autonomous separation maintenance.

Key words: air traffic management, autonomous separation control, horizontal cross-conflict, time-to-go of conflict point

CLC Number:

Daiwei LI, Xinmin TANG, Xiaona LU, Shengjia TANG. Aircraft autonomous separation control based on time-to-go of conflict point[J]. Systems Engineering and Electronics, 2024, 46(10): 3484-3491.

Figures/Tables 14

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Table 1

Fig.6

Fig.7

Fig.8

Fig.9

Fig.10

Fig.11

Fig.12

Fig.13

References 36

1	BALLIN M G, WING D J, HUGHES M F, et al. Airborne separation assurance and traffic management-research of concepts and technology[C]//Proc. of the Guidance, Navigation, and Control Conference and Exhibit, 1999: 313-324.
2	OCHIENG W Y , STUDIC M , MAJUMDAR A . Evolution of air traffic management concept of operations and its impact on the system architecture[J]. Transactions of Nanjing University of Aeronautics and Astronautics, 2016, 33 (4): 403- 413.
3	STASI L , MARCHITTO M , ANTOLI A , et al. Approximation of on-line mental workload index in ATC simulated multitasks[J]. Journal of Air Transport Management, 2010, 16 (6): 330- 333. doi: 10.1016/j.jairtraman.2010.02.004
4	ZHANG M , HANG Y . Integrated evluation of the safety controller's workload based on improved extension evaluation model[J]. Journal of Applied Sciences, 2013, 13 (15): 2969- 2973. doi: 10.3923/jas.2013.2969.2973
5	郝斯琪. 基于四维航迹运行下的航空器冲突探测与解脱方法研究[D]. 哈尔滨: 哈尔滨工业大学, 2020.
	HAO S Q. Research on aircraft conflict detection and resolution methods based on four-dimensional trajectory operation[D]. Harbin: Harbin Institute of Technology, 2020.
6	汤新民, 郑鹏程. 基于大地坐标系的IMM航空器短期航迹外推[J]. 系统工程与电子技术, 2022, 44 (7): 2293- 2301. doi: 10.12305/j.issn.1001-506X.2022.07.26
	TANG X M , ZHENG P C . Short term track extrapolation of IMM aircraft based on geodetic coordinate system[J]. Systems Engineering and Electronics, 2022, 44 (7): 2293- 2301. doi: 10.12305/j.issn.1001-506X.2022.07.26
7	ZHANG Y, CHU P, TIAN Y G, et al. A conflict-free trajectory generation method for pre-tactical phase in trajectory based operation[C]//Proc. of the IOP Conference Series: Earth and Environmental Science, 2020.
8	WEITZ L A. Derivation of a point-mass aircraft model used for fast-time simulation[R]. McLean: MITRE, 2015.
9	WEITZ L A . Investigating string stability of a time-history control law for interval management[J]. Transportation Research Part C: Emerging Technologies, 2013, 33, 257- 271. doi: 10.1016/j.trc.2011.10.002
10	SUN M H , RAND K , FLEMING C . 4D waypoint generation for conflict-free trajectory based operation[J]. Aerospace Science and Technology, 2019, 88, 350- 361. doi: 10.1016/j.ast.2019.03.035
11	王立超, 张洪海, 刘皞, 等. 面向意图的通用航空器冲突解脱自主决策方法[J]. 武汉理工大学学报(交通科学与工程版), 2020, 44 (5): 855- 858.
	WANG L C , ZHANG H H , LIU Z , et al. Intention oriented autonomous decision-making method for conflict resolution of general aircraft[J]. Journal of Wuhan University of Technology (Transportation Science & Engineering), 2020, 44 (5): 855- 858.
12	裴柯欣. 复杂天气条件下的无人飞行器航迹规划研究[D]. 广汉: 中国民用航空飞行学院, 2021.
	PEI K X, Research on UAV track planning under complex weather conditions[D]. Guanghan: China Civil Aviation Flight Academy, 2021.
13	MONDOLONI S , ROZEN N . Aircraft trajectory prediction and synchronization for air traffic management applications[J]. Progress in Aerospace Sciences, 2020, 119, 100640. doi: 10.1016/j.paerosci.2020.100640
14	HAO S Q , CHENG S W , ZHANG Y P . A multi-aircraft conflict detection and resolution method for 4D trajectory-based ope- ration[J]. Chinese Journal of Aeronautics, 2018, 31 (7): 1579- 1593. doi: 10.1016/j.cja.2018.04.017
15	TRAN P N , NGUYEN H Q V , PHAM D T , et al. Aircraft trajectory prediction with enriched intent using encoder-decoder architecture[J]. IEEE Access, 2022, 10, 17881- 17896. doi: 10.1109/ACCESS.2022.3149231
16	HU Y D , GAO C S , LI J L , et al. Novel trajectory prediction algorithms for hypersonic gliding vehicles based on maneuver mode on-line identification and intent inference[J]. Measurement Science and Technology, 2021, 32 (11): 115012. doi: 10.1088/1361-6501/ac1284
17	FU Q X , LIANG X L , ZHANG J Q , et al. Intent inference based trajectory prediction and smooth for UAS in low-altitude airspace with geofence[J]. CMC-Computers Materials & Continua, 2020, 63 (1): 417- 444.
18	SUN F , CHEN Y , XU X , et al. Velocity obstacle-based conflict resolution and recovery method[J]. The Aeronautical Journal, 2022, 126 (1296): 345- 364. doi: 10.1017/aer.2021.67
19	汤新民, 郑鹏程. 航路序贯飞行条件下的航空器自主间隔控制[J]. 南京航空航天大学学报, 2019, 51 (6): 742- 748.
	TANG X M , ZHENG P C . Aircraft autonomous interval control under the condition of route sequential flight[J]. Journal of Nanjing University of Aeronautics and Astronautics, 2019, 51 (6): 742- 748.
20	ZHANG Y , SHORTLE J , SHERRY L , et al. Methodology for collision risk assessment of an airspace flow corridor concept[J]. Reliability Engineering & System Safety, 2015, 142 (5): 444- 455.
21	TIAN Y , DONG Y L , YE B J , et al. A framework for the assessment of distributed self-separation procedures for air traffic in flow corridors[J]. IEEE Access, 2019, 7, 123544- 123557. doi: 10.1109/ACCESS.2019.2937655
22	MING Z H , ZHANG M , TANG X M , et al. Structural modelling and deceleration algorithm for a follow aircraft on performance-based navigation airway based on multi-agent technique[J]. Cybernetics and Information Technologies, 2015, 15 (6): 46- 56. doi: 10.1515/cait-2015-0066
23	王莉莉, 张新瑜, 张兆宁. 空中高速路交通流的跟驰现象及流量模型[J]. 西南交通大学学报, 2012, 47 (1): 158- 162.
	WANG L L , ZHANG X Y , ZHANG Z N . Car-following phenomenon and flow model of air highway traffic flow[J]. Journal of Southwest Jiaotong University, 2012, 47 (1): 158- 162.
24	BRITTAIN M , WEI P . Scalable autonomous separation assurance with heterogeneous multi-agent reinforcement learning[J]. IEEE Trans. on Automation Science and Engineering, 2022,
25	NAKAMURA Y , TAKEICHI N . A self-separation algorithm for width-limited high density air corridor[J]. Proc.of the Institution of Mechanical Engineers Part G-Journal of Aerospace Engineering, 2016, 230 (9): 1632- 1640. doi: 10.1177/0954410015620446
26	NAKAMURA Y , TAKEICHI N , KAGEYAMA K . A self-separation algorithm using relative speed for high density air corridor[J]. Transactions of the Japan Society for Aeronautical & Space Sciences, 2014, 57 (6): 336- 342.
27	NAKAMURA Y, TAKEICHI N. A self-separation algorithm for high-density air corridor allocated to optimal flight trajectories[C]//Proc. of the AIAA Modeling and Simulation Techno-logies Conference, 2016.
28	XIE Y B , PONGSAKORNSATHIEN N , GARDI A , et al. Explanation of machine-learning solutions in air-traffic management[J]. Aerospace, 2021, 8 (8): 224. doi: 10.3390/aerospace8080224
29	GUO W, BRITTAIN M, WEI P. Safety enhancement for deep reinforcement learning in autonomous separation assurance[C]// Proc. of the IEEE International Intelligent Transportation Systems Conference, 2021: 348-354.
30	DOBRUSZKES F . Why do planes not fly the shortest routes? A review[J]. Applied Geography, 2019, 109, 102033. doi: 10.1016/j.apgeog.2019.06.001
31	RTCA DO 361. Minimum operational performance standards (MOPS) for flight-deck interval management (FIM)[S]. Washington Radio Technical Commission for Aeronautics, 2015.
32	FRANCO A, RIVAS D, VALENZUELA A. Optimal aircraft path planning considering wind uncertainty[C]//Proc. of the 7th European Conference for Aeronautics and Space Sciences, 2017: 3-6.
33	VALENZUELA A, FRANCO A, RIVAS D. Sector demand analysis under meteorological uncertainty[C]//Proc. of the 7th European Conference for Aeronautics and Space Sciences, 2017: 1-15.
34	JIANG S Y , LUO X , HE L . Research on method of trajectory prediction in aircraft flight based on aircraft performance and historical track data[J]. Mathematical Problems in Engineering, 2021, 2021, 11.
35	LI R B , LU C , LIU J Y , et al. Air data estimation algorithm under unknown wind based on information fusion[J]. Journal of Aerospace Engineering, 2018, 31 (5): 04018072. doi: 10.1061/(ASCE)AS.1943-5525.0000889
36	Eurocontol Experimental Center. Model accuracy summary report for the base of aircraft data (BADA), Revision 3.10[R]. Brest: Eurocontrol Experimental Central, 2012.

航路点	x/km	y/km	v/(km/h)
1	100	100	800.00
2	160	130	801.46
3	220	160	802.25
4	280	190	802.98
5	340	220	803.75
6	400	250	804.67

[1]	Kexin BI, Minggong WU, Xiangxi WEN, Wenbin ZHANG, Wenda YANG. Conflict resolution strategy based on flight conflict network and genetic algorithm [J]. Systems Engineering and Electronics, 2023, 45(5): 1429-1440.
[2]	Xinmin TANG, Pengcheng ZHENG. IMM aircraft short-term track extrapolation based on geodetic coordinate system [J]. Systems Engineering and Electronics, 2022, 44(7): 2293-2301.
[3]	SU Zhi-gang, FU Xiao-xian, HAO Jing-tang. Dynamic triangulation based method for screening potential conflicts [J]. Systems Engineering and Electronics, 2016, 38(6): 1456-1461.

Aircraft autonomous separation control based on time-to-go of conflict point

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 14

References 36

Related Articles 3

Recommended Articles

Metrics

Comments