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摘要: 为了解决机场道面实际除冰雪作业中方案不能完全适应环境的问题, 考虑了除冰雪过程中作业方式和航空器适航条件, 构建了具有时间约束的两阶段除冰雪作业模型; 基于机场除冰雪车辆的作业能力, 研究了机械除冰雪作业方法中多车辆的协同作业问题, 设计了基于复拉普拉斯矩阵的队形控制模型; 为了减少通讯消耗及保证通讯稳定性, 基于Henneberg序列操作方法生成机场道面除冰雪作业车辆最优通讯图, 并验证了所生成最优通讯图满足队形控制模型所要求双根条件。研究结果表明: 两阶段除冰雪作业模型能够选择不同的异构车辆进行编队作业以达到时间和效果最优; 基于复拉普拉斯矩阵和领航者方法相结合得到的控制模型与传统控制模型相比队形更稳定; 采用边有向化操作所生成的最优通讯图保证了队形中领航者和跟随者之间通讯的有效性; 在一阶运动学模型下, 基于5自主体“人”字形编队从任意位置出发能够在1 min内实现速度收敛一致及生成期望队形, 且运动轨迹中不存在绕圈、小角度转弯的情况, 符合实际作业车辆运行规则, 并能在随后的作业中保持期望队形。可见, 所构建的队形控制模型能够实现对大型异构除冰雪作业车辆的队形控制, 满足预期要求。Abstract: In order to solve the problem that the scheme of the actual deicing operation of the airport pavement cannot be fully adapted to the environment, the operation mode and the airworthiness condition of the aircraft during deicing process were considered, and a two-stage deicing operation model with time constraints was constructed. Based on the operation ability of the airport deicing vehicle, the collaborative operation problem of multiple vehicles in the mechanical deicing operation method was studied, and the formation control model based on the complex Laplacian matrix was designed. In order to reduce communication consumption and ensure communication stability, the Henneberg sequence operation method was used to generate the optimal communication diagram of the airport pavement deicing operation vehicles, and the generated optimal communication diagram satisfied the double root condition required by the formation control model. Analysis result shows that the two-stage deicing operation model can select different heterogeneous vehicles for formation work to achieve the optimal time and effect. The formation of control model based on the combination of the complex Laplacian matrix and the leader method is more stable than the traditional control model. The optimal communication diagram generated by the edge directed ensures the availability of communication between the leader and follower in the formation. Under the first-order kinematics model, the speed convergence can be achieved and the desired formation can be generated within 1 min based on the 5-agent "人" type formation. There is no winding or small angle turning in the motion trajectory, which conforms to the actual operation rules of vehicles and can maintain the desired formation in subsequent operations. Therefore, the formation control model can realize the formation control of large-scale heterogeneous deicing operation vehicles and meet the expected requirements.
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[1] LIU Gang, GE Yong-feng, QIU T Z, et al. Optimization of snow plowing cost and time in an urban environment: a case study for the city of Edmonton[J]. Canadian Journal of Civil Engineering, 2014, 41 (7): 667-675. doi: 10.1139/cjce-2013-0409 [2] HAJIBABAI L. Scheduling and routing of service trucks and planning of resource replenishment locations for winter roadway maintenance[D]. Urbana: University of Illinois, 2014. [3] QUIRION-BLAIS O, LANGEVIN A, LEHUÉDÉ F, et al. Solving the large-scale min-max K-rural postman problem for snow plowing[J]. Networks, 2017, 70 (3): 195-215. doi: 10.1002/net.21759 [4] XIE Bing-lei, JIN Lei, MU Wei. Snow disposal operations optimization in winter highway maintenance[C]//IEEE. 21th Annual International Conference on Management Science and Engineering. New York: IEEE, 2014: 227-232. [5] XIE Bing-lei, LI Ying, JIN Lei. Vehicle routing optimization for deicing salt spreading in winter highway maintenance[J]. Procedia-Social and Behavioral Sciences, 2013, 96: 945-953. doi: 10.1016/j.sbspro.2013.08.108 [6] KINABLE J, VAN HOEVE W J, SMITH S F. Optimization models for a real-world snow plow pouting problem[C]//QUIMPER C G. Proceedings of 13th International Conference on Integration of Artificial Intelligence and Operations Research Techniques in Constraint Programming (CPAIOR 2016). Pittsburgh: Carnegie Mellon University, 2016: 229-245. [7] AKBARI V, SALMAN F S. Multi-vehicle synchronized arc routing problem to restore post-disaster network connectivity[J]. European Journal of Operational Research, 2017, 257 (2): 625-640. doi: 10.1016/j.ejor.2016.07.043 [8] LU Song, XU Jin-yu, BAI Er-lei, et al. Investigating microwave deicing efficiency in concrete pavement[J]. RSC Advances, 2017, 7 (15): 9152-9159. doi: 10.1039/C6RA27109J [9] MALEKI P, IRANPOUR B, SHAFABAKHSH G. Investigation of de-icing of roads with conductive concrete pavement containing carbon fibre-reinforced polymer (CFRP)[J]. International Journal of Pavement Engineering, 2019, 20 (6): 682-690. doi: 10.1080/10298436.2017.1326235 [10] YAO Xu-dan, HAWKINS S C, FALZON B G. An advanced anti-icing/de-icing system utilizing highly aligned carbon nanotube webs[J]. Carbon, 2018, 136: 130-138. doi: 10.1016/j.carbon.2018.04.039 [11] YI Yi, YU Jian-ying, CHEN Xiao, et al. Preparation and properties of a self-deicing coating based on layered double hydroxide[C]//Trans Tech Publications Ltd. . 17th IUMRS International Conference in Asia. Zurich: Trans Tech Publications Ltd., 2017: 1553-1560. [12] CHEN Hua-xin, WU Yong-chang, XIA Hui-yun, et al. Review of ice-pavement adhesion study and development of hydrophobic surface in pavement deicing[J]. Journal of Traffic and Transportation Engineering (English Edition), 2018, 5 (3): 224-238. doi: 10.1016/j.jtte.2018.03.002 [13] 邢志伟, 陈亚雄, 罗谦, 等. 枢纽机场道面除冰雪机群资源优化配置[J]. 中国民航大学学报, 2017, 35 (6): 36-40, 51. doi: 10.3969/j.issn.1674-5590.2017.06.008XING Zhi-wei, CHEN Ya-xiong, LUO Qian, et al. Optimization and allocation research of hub airport pavement fleet deicing resource[J]. Journal of Civil Aviation University of China, 2017, 35 (6): 36-40, 51. (in Chinese). doi: 10.3969/j.issn.1674-5590.2017.06.008 [14] FAGHRI A, LICHLITER A, FAGHRI A, et al. Assessing airport snow and ice removal and its economic implications for sustainable airport management[J]. Journal of Airport Management, 2014, 8 (2): 174-188. [15] PERRIER N, LANGEVIN A, CAMPBELL J F. A survey of models and algorithms for winter road maintenance. Part I: system design for spreading and plowing[J]. Computers and Operations Research, 2006, 33 (1): 209-238. doi: 10.1016/j.cor.2004.07.006 [16] 王祥科, 李迅, 郑志强. 多智能体系统编队控制相关问题研究综述[J]. 控制与决策, 2013, 28 (11): 1601-1613. https://www.cnki.com.cn/Article/CJFDTOTAL-KZYC201311001.htmWANG Xiang-ke, LI Xun, ZHENG Zhi-qiang. Survey of developments on multi-agent formation control related problems[J]. Control and Decision, 2013, 28 (11): 1601-1613. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-KZYC201311001.htm [17] WANG He-sheng, GUO De-jun, LIANG Xin-wu, et al. Adaptive vision-based leader-follower formation control of mobile robots[J]. IEEE Transactions on Industrial Electronics, 2017, 64 (4): 2893-2903. doi: 10.1109/TIE.2016.2631514 [18] WANG Xiang-ke, ZENG Zhi-wei, CONG Yi-rui. Multi-agent distributed coordination control: developments and directions via graph view point[J]. Neurocomputing, 2016, 199: 204-218. doi: 10.1016/j.neucom.2016.03.021 [19] SALEHIZADEH M, DILLER E. Two-agent formation control of magnetic micro-robots in two dimensions[J]. Journal of Micro-Bio Robotics, 2017, 12 (1-4): 9-19. doi: 10.1007/s12213-017-0095-5 [20] ALONSO-MORA J, BAKER S, RUS D. Multi-robot formation control and object transport in dynamic environments via constrained optimization[J]. International Journal of Robotics Research, 2017, 36 (9): 1000-1021. doi: 10.1177/0278364917719333 [21] OH K K, PARK M C, AHN H S. A survey of multi-agent formation control[J]. Automatica, 2015, 53: 424-440. doi: 10.1016/j.automatica.2014.10.022 [22] WANG Li-li, HAN Zhi-min, LIN Zhi-yun. Formation control of directed multi-agent networks based on complex Laplacian[C]//IEEE. Proceedings of the 51st IEEE Conference on Decision and Control. New York: IEEE, 2012: 5292-5297. [23] HAN Zhi-min, LIN Zhi-yun, FU Min-yue, et al. Distributed coordination in multi-agent systems: a graph Laplacian perspective[J]. Frontiers of Information Technology and Electronic Engineering, 2015, 16 (6): 429-448. doi: 10.1631/FITEE.1500118 [24] LIN Zhi-yun, WANG Li-li, HAN Zhi-min, et al. Distributed formation control of multi-agent systems using complex Laplacian[J]. IEEE Transactions on Automatic Control, 2014, 59 (7): 1765-1777. doi: 10.1109/TAC.2014.2309031 [25] 罗小元, 邵士凯, 关新平, 等. 多智能体最优持久图编队动态生成与控制[J]. 自动化学报, 2013, 39 (9): 1431-1438. https://www.cnki.com.cn/Article/CJFDTOTAL-MOTO201309005.htmLUO Xiao-yuan, SHAO Shi-kai, GUAN Xin-ping, et al. Dynamic generation and control of optimally persistent formation for multi-agent systems[J]. Acta Automatica Sinica, 2013, 39 (9): 1431-1438. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-MOTO201309005.htm [26] HENDRICKX J M, ANDERSON B D O, DELVENNE J C, et al. Directed graphs for the analysis of rigidity and persistence in autonomous agent systems[J]. International Journal of Robust and Nonlinear Control, 2007, 17 (10/11): 960-981. [27] 罗小元, 杨帆, 李绍宝, 等. 多智能体系统的最优持久编队生成策略[J]. 自动化学报, 2014, 40 (7): 1311-1319. https://www.cnki.com.cn/Article/CJFDTOTAL-MOTO201407006.htmLUO Xiao-yuan, YANG Fan, LI Shao-bao, et al. Generation of optimally persistent formation for multi-agent systems[J]. Acta Automatica Sinica, 2014, 40 (7): 1311-1319. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-MOTO201407006.htm [28] 刘春, 宗群, 窦立谦. 基于持久图的双轮机器人编队生成与控制[J]. 控制工程, 2017, 24 (3): 518-523. https://www.cnki.com.cn/Article/CJFDTOTAL-JZDF201703008.htmLIU Chun, ZONG Qun, DOU Li-qian. Generation and control of wheeled robots formation based on persistent graph theory[J]. Control Engineering of China, 2017, 24 (3): 518-523. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-JZDF201703008.htm [29] MENG De-yuan, JIA Ying-min, DU Jun-ping, et al. On iterative learning algorithms for the formation control of nonlinear multi-agent systems[J]. Automatica, 2014, 50 (1): 291-295. doi: 10.1016/j.automatica.2013.11.009 [30] YANG Tao, MENG Zi-yang, DIMAROGONAS D V. Global consensus for discrete-time multi-agent systems with input saturation constrains[J]. Automatica, 2014, 50: 499-506. doi: 10.1016/j.automatica.2013.11.008 -
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