飞机地面除冰作业协同控制仿真研究

doi:10.16182/j.issn1004731x.joss.22-0096

系统仿真学报 ›› 2023, Vol. 35 ›› Issue (6): 1157-1168.doi: 10.16182/j.issn1004731x.joss.22-0096

飞机地面除冰作业协同控制仿真研究

王立文¹(), 李彪¹^,², 邢志伟²(), 廉冠³

^1.中国民航大学航空工程学院，天津 300300
^2.中国民航大学电子信息与自动化学院，天津 300300
^3.桂林电子科技大学建筑与交通工程学院，广西桂林 541004

收稿日期:2022-02-08 修回日期:2022-03-28 出版日期:2023-06-29 发布日期:2023-06-20
通讯作者: 邢志伟 E-mail:cauc_wlw@126.com;cauc_xzw@163.com
作者简介:王立文(1962-)，男，教授，博导，博士，研究方向为机场运行安全保障技术。E-mail：cauc_wlw@126.com
基金资助:
国家重点研发计划(2018YFB1601200);广西区青年科学基金(2019JJB170004)

Simulation Research on Cooperative Control for Aircraft Ground Deicing Operation

Liwen Wang¹(), Biao Li¹^,², Zhiwei Xing²(), Guan Lian³

^1.College of Aeronautical Engineering, Civil Aviation University of China, Tianjin 300300, China
^2.College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China
^3.School of Architecture and Transportation Engineering, Guilin University of Electronic Technology, Guilin 541004, China

Received:2022-02-08 Revised:2022-03-28 Online:2023-06-29 Published:2023-06-20
Contact: Zhiwei Xing E-mail:cauc_wlw@126.com;cauc_xzw@163.com

摘要/Abstract

摘要：

针对飞机地面除冰作业过程协同性较弱及效率低下的问题，设计一种信息状态一致性的除冰作业协同控制 方法。提出两阶段式除冰Agent模型并建立除冰效果最优、除冰时间最短的多目标协同优化模型，采用拉格朗日松弛算法构造最优作业参数可行解。结合除冰工艺及模式研究基于运行拓扑的除冰作业协作图，构建改进一致性的地面除冰协同控制方法。参照实际情况开展仿真验证，结果表明：一致性控制策略能够以最优的除冰效果将作业位置误差保持在1 m内，协同状态稳定在最大振幅范围，且能满足不同模态要求，与自适应和领航者2种控制方法对比精确度最大提升7.44%。

关键词: 航空运输, 协同控制, 拉格朗日松弛, 改进一致性控制, 飞机地面除冰

Abstract:

Aiming at the weak cooperation and low efficiency in aircraft ground deicing operation, a cooperative control method for deicing operations is designed based on the consistency of information states. A two-stage deicing Agent model is proposed and a multi-objective collaborative optimization model with the best deicing effect and the shortest deicing time is established. Lagrangian relaxation algorithm is applied to construct a feasible solution for the optimal operating parameters. Combined with the deicing process and mode, the cooperative graph of deicing operation is studied based on the operating topology, and the cooperative control method of ground deicing is constructed based on improved consistency is constructed, and the simulation verification is carried out according to the actual situation. The result shows that the consistency control policy can keep the operating position error within 1 m, the coordination ability is stable in the maximum amplitude range, and the requirements of different modes can be meet. The average accuracy is improved by up to 7.44% compared with the two control methods of adaptive and navigator.

Key words: air transportation, collaborative control, Lagrangian relaxation, improved consistency control, aircraft ground deicing

中图分类号:

TP274.2

王立文, 李彪, 邢志伟, 廉冠. 飞机地面除冰作业协同控制仿真研究[J]. 系统仿真学报, 2023, 35(6): 1157-1168.

Liwen Wang, Biao Li, Zhiwei Xing, Guan Lian. Simulation Research on Cooperative Control for Aircraft Ground Deicing Operation[J]. Journal of System Simulation, 2023, 35(6): 1157-1168.

图/表 15

图1

图2

表1

表2

表3

图3

图4

图5

图6

图7

图8

图9

表4

表5

图10

参考文献 24

1	Weiszer M, Burke E K, Chen Jun. Multi-Objective Routing and Scheduling for Airport Ground Movement[J]. Transportation Research Part C: Emerging Technologies, 2020, 119: 102734.
2	Malandri C, Mantecchini L, Paganelli F, et al. Impacts of Unplanned Aircraft Diversions on Airport Ground Operations[J]. Transportation Research Procedia, 2020, 47: 537-544.
3	鲍帆, 蒋伟煜. 基于机场协同决策(A-CDM)的除冰管理研究[C]//第一届空中交通管理系统技术学术年会论文集. 江苏南京: 中国指挥与控制学会, 2018: 330-334.
	Bao Fan, Jiang Weiyu. Research on deicing management Based on airport CDM[C]//Proceedings of the First Annual Academic Conference on Air Traffic Management System Technology. Nanjing, Jiangsu: Chinese Institute of Command and Control, 2018: 330-334.
4	邢志伟, 李斯, 罗谦. 机场道面除冰雪车辆队形控制模型[J]. 交通运输工程学报, 2019, 19(4): 182-190.
	Xing Zhiwei, Li Si, Luo Qian. Formation Control Model of Airport Pavement Deicing Vehicles[J]. Journal of Traffic and Transportation Engineering, 2019, 19(4): 182-190.
5	Zhao Xiaoyun, Darwish R, Pernestål A. Automated Vehicle Traffic Control Tower: A Solution to Support the Next Level Automation[J]. International Journal of Transport and Vehicle Engineering, 2020, 14(7): 283-293.
6	Wang Shixiong, Che Yuxin, Zhao Huangjie, et al. Accurate Tracking, Collision Detection, and Optimal Scheduling of Airport Ground Support Equipment[J]. IEEE Internet of Things Journal, 2021, 8(1): 572-584.
7	Kamel M A, Yu Xiang, Zhang Youmin. Formation Control and Coordination of Multiple Unmanned Ground Vehicles in Normal and Faulty Situations: A Review[J]. Annual Reviews in Control, 2020, 49: 128-144.
8	Bertsimas D, Jaillet P, Martin S. Online Vehicle Routing: The Edge of Optimization in Large-scale Applications[J]. Operations Research, 2019, 67(1): 143-162.
9	吴宇, 梁天骄. 基于改进一致性算法的无人机编队控制[J]. 航空学报, 2020, 41(9): 167-185.
	Wu Yu, Liang Tianjiao. Improved Consensus-based Algorithm for Unmanned Aerial Vehicle Formation Control[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(9): 167-185.
10	闫冬, 陈盛, 彭国政, 等. 基于层次深度强化学习的带电作业机械臂控制技术[J]. 高电压技术, 2020, 46(2): 459-470.
	Yan Dong, Chen Sheng, Peng Guozheng, et al. Live Working Manipulator Control Technology Based on Hierarchical Deep Reinforcement Learning[J]. High Voltage Engineering, 2020, 46(2): 459-470.
11	徐广飞, 陈美舟, 苗河泉, 等. 基于模型预测控制的农机主从跟随作业控制方法[J]. 农业机械学报, 2020, 51(增2): 11-20.
	Xu Guangfei, Chen Meizhou, Miao Hequan, et al. Following Operation Control Method of Farmer Machinery Based on Model Predictive Control[J]. Transactions of the Chinese Society for Agricultural Machinery, 2020, 51(S2): 11-20.
12	刘源, 许宇豪. 基于免疫Agent算法的无人艇协同作战研究[J]. 军事交通学院学报, 2020, 22(6): 88-91.
	Liu Yuan, Xu Yuhao. On Cooperative Operation of Unmanned Surface Vehicle Based on Immune Agent Algorithm[J]. Journal of Military Transportation University, 2020, 22(6): 88-91.
13	Zhu Sheng, Aksun-Guvenc B. Trajectory Planning of Autonomous Vehicles Based on Parameterized Control Optimization in Dynamic on-Road Environments[J]. Journal of Intelligent & Robotic Systems, 2020, 100(3): 1055-1067.
14	Sayyaadi H, Soltani A. Modeling and Control for Cooperative Transport of a Slung Fluid Container Using Quadrotors[J]. Chinese Journal of Aeronautics, 2018, 31(2): 262-272.
15	Janssen S, Sharpanskykh A, Curran R. Agent-Based Modelling and Analysis of Security and Efficiency in Airport Terminals[J]. Transportation Research Part C: Emerging Technologies, 2019, 100: 142-160.
16	Ramezani A, Malek M R, Mohammadi N. Optimizing Airport Service Performance by Spatial Multi Objective Location Allocation Problem[J]. Spatial Information Research, 2021, 29(3): 309-318.
17	江峰, 倪少权, 吕红霞. 基于拉格朗日松弛的高速铁路列车运行图新增运行线局部调整模型[J]. 交通运输系统工程与信息, 2018, 18(4): 163-170.
	Jiang Feng, Ni Shaoquan, Hongxia Lü. A High-speed Railway New-added Train Timetable Partial Adjustment Model Based on Lagrangian Relaxation[J]. Journal of Transportation Systems Engineering and Information Technology, 2018, 18(4): 163-170.
18	Sharma A, Chinnery D, Reimann T, et al. Fast Lagrangian Relaxation-Based Multithreaded Gate Sizing Using Simple Timing Calibrations[J]. IEEE Transactions on Computer-aided Design of Integrated Circuits and Systems, 2020, 39(7): 1456-1469.
19	吕天佟, 王登峰, 王传青. 隐式参数化白车身多目标协同优化设计[J]. 北京理工大学学报, 2019, 39(5): 447-453.
	Tiantong Lü, Wang Dengfeng, Wang Chuanqing. Multi-objective Lightweight Collaborative Optimization and Design for Latent Parametric BIW Structure[J]. Transactions of Beijing Institute of Technology, 2019, 39(5): 447-453.
20	陈世明, 管俊杰, 高彦丽, 等. 组合连通拓扑下基于事件触发的多智能体快速一致性算法[J]. 自动化学报, 2018, 44(12): 2269-2277.
	Chen Shiming, Guan Junjie, Gao Yanli, et al. Event-triggered Fast Consensus Algorithm for Multi-agent Systems Under Jointly-connected Topology[J]. Acta Automatica Sinica, 2018, 44(12): 2269-2277.
21	Sun Guibin, Zhou Rui, Xu Kun, et al. Cooperative Formation Control of Multiple Aerial Vehicles Based on Guidance Route in a Complex Task Environment[J]. Chinese Journal of Aeronautics, 2020, 33(2): 701-720.
22	Lin Yuchen, Nguyen H L T. Adaptive Neuro-fuzzy Predictor-based Control for Cooperative Adaptive Cruise Control System[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 21(3): 1054-1063.
23	Gopalakrishnan R, Guevara C A, Ben-Akiva M. Combining Multiple Imputation and Control Function Methods to Deal with Missing Data and Endogeneity in Discrete-choice Models[J]. Transportation Research Part B: Methodological, 2020, 142: 45-57.
24	Gonçalves L, Navarro J B, Sala R. Spanish Validation of the Benchmark Resilience Tool (Short-form Version) to Evaluate Organisational Resilience[J]. Safety Science, 2019, 111: 94-101.

参数名称	A350	A330	B737	B787
机身宽度	5.96	6.00	5.16	5.77
机身长度	66.80	58.82	37.81	56.72
翼展	64.75	60.30	28.90	60.12
翼尖至机头长度	48.68	42.82	18.68	40.36
除冰安全距离	14.00	14.00	11.00	15.00

区域	起始点	转折点1	转折点2	终止点
A	(32.38, 7.45)	(16.98, 14.90)	(16.98, 48.68)	(32.38, 48.68)
B	(-32.38, 7.45)	(-16.98, 14.90)	(-16.98, 48.68)	(-32.38, 48.68)
C	(-32.38, -7.45)	(-16.98, -7.45)	(-16.98, -34.82)	(-32.38, -42.27)
D	(32.38, -7.45)	(16.98, -7.45)	(16.98, -34.82)	(32.38, -42.27)

参数	慢车	关车
速度/(m/s)	0.8	1.0
振幅/m	1.0	1.5
频率/Hz	0.8	1.5

机型	类型	模式	除冰效果/m	作业时间/s	平均作业误差/m	平均协同状态/m
A350	关车	双车	1.932	369.486	0.515	0.559
	关车	四车	2.113	208.332	0.461	0.397
	慢车	双车	1.917	276.233	0.459	0.432
	慢车	四车	1.884	169.715	0.397	0.264
A330	关车	双车	2.282	342.387	0.735	0.574
	关车	四车	2.310	183.057	0.728	0.405
	慢车	双车	2.168	263.771	0.713	0.443
	慢车	四车	2.500	150.936	0.700	0.366
B737	关车	双车	2.830	306.653	1.252	0.893
	关车	四车	2.654	204.523	0.519	0.697
	慢车	双车	2.018	235.588	0.987	0.759
	慢车	四车	2.159	136.256	0.476	0.693
B787	关车	双车	1.901	364.286	0.522	0.554
	关车	四车	2.089	201.034	0.487	0.394
	慢车	双车	1.912	265.844	0.491	0.423
	慢车	四车	1.870	149.039	0.466	0.360

属性	贪婪算法	遗传算法	拉格朗日松弛算法
速度/(m/s)	0.884	0.642	0.481
振幅/m	1.002	0.739	0.813
频率/Hz	1.880	1.225	1.423
作业误差/m	1.673	0.936	0.515
协同状态/m	1.393	0.758	0.559
协同稳定性/m	1.438	0.823	0.442

飞机地面除冰作业协同控制仿真研究

Simulation Research on Cooperative Control for Aircraft Ground Deicing Operation

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 15

参考文献 24

相关文章 7

编辑推荐

Metrics

本文评价

[1]	李向阳, 张志利, 张宁, 桂毅, 李嘉. 局部信息联通的多无人机协同控制过程仿真研究[J]. 系统仿真学报, 2021, 33(7): 1501-1513.
[2]	张云荣, 张志利, 李向阳, 王红光, 米文鹏, 孔祥通, 刘朋朋. 基于Agent理论的多无人地面平台协同控制仿真研究[J]. 系统仿真学报, 2020, 32(7): 1211-1219.
[3]	邢志伟, 李彪, 朱慧, 罗谦. 基于深度神经网络的航班保障时间预测研究[J]. 系统仿真学报, 2020, 32(4): 678-686.
[4]	邢志伟, 蒋骏贤, 罗晓, 罗谦, 杨扬. 离港值机排队聚集规律及队列模型研究[J]. 系统仿真学报, 2019, 31(8): 1548-1554.
[5]	朱政泽, 周海鹰, 付勇智, 周奎, 王思山, 龚家元, 邓思豪, 侯昆明. 基于延迟补偿的网联式自主驾驶车辆协同控制[J]. 系统仿真学报, 2019, 31(7): 1448-1459.
[6]	陈斌, 王立文. 基于航班动态的飞机地面除冰装备数量优化[J]. 系统仿真学报, 2019, 31(4): 757-764.
[7]	王梦琳, 孙涛, 郑松林, 孙跃东. 基于底盘子系统协同控制的车辆防侧翻性能分析[J]. 系统仿真学报, 2015, 27(1): 163-170.