飞机地面除冰资源协同控制

doi:10.16183/j.cnki.jsjtu.2020.342

上海交通大学学报 ›› 2021, Vol. 55 ›› Issue (11): 1362-1370.doi: 10.16183/j.cnki.jsjtu.2020.342

所属专题：《上海交通大学学报》2021年12期专题汇总专辑；《上海交通大学学报》2021年“航空航天科学技术”专题

飞机地面除冰资源协同控制

李彪¹^,², 王立文¹(), 邢志伟², 王思博², 罗谦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.中国民用航空局第二研究所工程技术研究中心, 成都 610041

收稿日期:2020-10-21 出版日期:2021-11-28 发布日期:2021-12-03
通讯作者: 王立文 E-mail:cauc_wlw@126.com
作者简介:李彪(1993-),男,河北省张家口市人,博士生,从事机场运行安全保障技术研究.
基金资助:
国家重点研发计划(2018YFB1601200);中央高校基本科研业务费中国民航大学专项项目(3122019094);中国民航大学研究生科研创新项目(205014060219);天津市研究生科研创新项目(2020YJSB098)

Cooperative Control of Aircraft Ground Deicing Resources

LI Biao¹^,², WANG Liwen¹(), XING Zhiwei², WANG Sibo², LUO Qian³

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. Engineering Technology Research Center, The Second Research Institute of Civil Aviation Administration of China, Chengdu 610041, China

Received:2020-10-21 Online:2021-11-28 Published:2021-12-03
Contact: WANG Liwen E-mail:cauc_wlw@126.com

摘要/Abstract

摘要：

针对多并行除冰任务下分布式资源协同能力较弱及均衡性低的问题,结合机场除冰资源配置及时空分布状态,提出了一种基于多Agent协商的飞机地面除冰资源协同控制方法.建立了多Agent除冰资源协同运行框架,设计了面向全局协同联合体招投标机制的资源优化方法,提升了整体任务均衡性.在协同运行方案的基础上构建自治多Agent协同优化模型,采用加入决策因子的模型预测控制方法生成自治协同控制策略,并面向实际场景验证所提方法的可行性.结果表明,基于优化方案生成的初始化协同控制策略容错时间均值达4.89 min,与其他传统方法相比,平均起飞容限最大提升1.015 min,平均利用率增加15.28%,保证了除冰资源的安全性及协同性.

关键词: 航空运输, 协同控制, 多Agent协商, 模型预测控制, 地面除冰资源

Abstract:

Aimed at the problem of weak coordination and low balance of distributed resources under multiple parallel deicing tasks, a cooperative control method of aircraft ground deicing resources based on multi-agent negotiation was proposed, which combined airport deicing resource allocation and space-time distribution. A framework for collaborative operation of multi-agent deicing resources was established, and a resource optimization method for the bidding mechanism of a global collaborative consortium was designed to improve the overall task balance. Based on the operating plan, an autonomous multi-agent resource collaborative optimization model was constructed. The model predictive control method was applied to generate a collaborative control strategy, and the feasibility was verified in actual scenarios. The results demonstrate that the resource coordination and anti-interference ability of the proposed method are significantly enhanced while meeting the real-time requirements. Compared with the results obtained by other methods, the average takeoff tolerance is 4.89 min, increased by 1.015 min, and the average utilization rate is increased by 15.28%, which can ensure the safety and synergy of deicing resources.

Key words: air transportation, collaborative control, multi-Agent negotiation, model predictive control, ground deicing resources

中图分类号:

V351.392
U8

李彪, 王立文, 邢志伟, 王思博, 罗谦. 飞机地面除冰资源协同控制[J]. 上海交通大学学报, 2021, 55(11): 1362-1370.

LI Biao, WANG Liwen, XING Zhiwei, WANG Sibo, LUO Qian. Cooperative Control of Aircraft Ground Deicing Resources[J]. Journal of Shanghai Jiao Tong University, 2021, 55(11): 1362-1370.

图/表 16

图1

图2

图3

图4

图5

表1

图6

表2

图7

表3

图8

图9

图10

图11

表4

仿真优化参数对比结果

除冰坪		S₁	S₂	S₃	S₄
r_t	I	9.24	11.51	9.46	11.57
	II	9.87	12.27	10.80	12.86
	III	10.22	12.74	10.14	11.04
$e -$	I	0.609	0.660	0.666	0.712
	II	0.571	0.613	0.457	0.608
	III	0.404	0.557	0.388	0.321

表4

图12

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除冰坪编号	除冰机位数量/个	兼容除冰机位/个	除冰车量编队数/个
1	4	2	4
2	6	3	6
3	5	3	5
4	3	3	6
5	6	3	9
6	4	3	5
7	4	3	5

除冰坪编号	保障能力	综合补液能力	平均正点率	综合得分
1	74.688	90.053	83.333	82.691
2	81.429	86.010	85.714	84.384
3	78.889	80.230	87.143	82.087
4	57.708	88.820	86.667	77.732

除冰坪	单独除冰任务	联合体除冰任务	联合编队
S₁	M₄, M₆, M₁₁, M₁₅, M₂₀, M₃₀, M₃₉, M₄₉, M₅₄	M₂₁, M₃₂, M₄₀, M₅₁	V₅
S₂	M₁, M₅, M₈, M₁₀, M₁₃, M₁₆, M₂₆, M₃₁, M₃₅, M₄₂, M₄₄, M₅₅	M₁₉, M₂₉, M₃₆, M₃₈, M₄₈, M₅₃	V₅
S₃	M₂, M₇, M₉, M₁₂, M₁₄, M₂₂, M₃₃, M₃₄, M₄₁, M₄₃, M₄₇, M₅₂	M₁₈, M₂₈, M₃₇	V₆
S₄	M₃, M₁₇, M₂₃, M₂₄, M₄₅, M₄₆, M₅₀	M₂₅, M₂₇	V₇

飞机地面除冰资源协同控制

Cooperative Control of Aircraft Ground Deicing Resources

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