针对多机场进场航班协同调度问题,以协同决策(collaborative decision making,CDM)理念为基础,在重点分析各航空公司之间排序公平性的基础上,提出了一种基于按时刻表分配(ration by schedule,RBS)公布顺序的离散化优化模型.该模型通过...针对多机场进场航班协同调度问题,以协同决策(collaborative decision making,CDM)理念为基础,在重点分析各航空公司之间排序公平性的基础上,提出了一种基于按时刻表分配(ration by schedule,RBS)公布顺序的离散化优化模型.该模型通过分析多机场终端区定位点和跑道双重约束,均衡各航空公司航班相对RBS次序位置变动数,实现了提高调度公平性、优化调度延误时间、减少航班改变位置架次的多目标优化.将模糊自修正多目标粒子群算法(FS-MOPSO)应用于模型进行求解计算,并对上海多机场终端区航班调度进行仿真模拟,结果表明:两机场的30架进场航班调度延误时间较传统先到先服务方案减少22.53%;各航空公司航班改变位置架次偏差值较单一以延误最优遗传算法仿真结果降低26.31%.展开更多
A new intelligent anti-swing control scheme,which combined fuzzy neural network(FNN) and sliding mode control(SMC) with particle swarm optimization(PSO),was presented for bridge crane.The outputs of three fuzzy neural...A new intelligent anti-swing control scheme,which combined fuzzy neural network(FNN) and sliding mode control(SMC) with particle swarm optimization(PSO),was presented for bridge crane.The outputs of three fuzzy neural networks were used to approach the uncertainties of the positioning subsystem,lifting-rope subsystem and anti-swing subsystem.Then,the parameters of the controller were optimized with PSO to enable the system to have good dynamic performances.During the process of high-speed load hoisting and dropping,this method can not only realize the accurate position of the trolley and eliminate the sway of the load in spite of existing uncertainties,and the maximum swing angle is only ±0.1 rad,but also completely eliminate the chattering of conventional sliding mode control and improve the robustness of system.The simulation results show the correctness and validity of this method.展开更多
文摘针对多机场进场航班协同调度问题,以协同决策(collaborative decision making,CDM)理念为基础,在重点分析各航空公司之间排序公平性的基础上,提出了一种基于按时刻表分配(ration by schedule,RBS)公布顺序的离散化优化模型.该模型通过分析多机场终端区定位点和跑道双重约束,均衡各航空公司航班相对RBS次序位置变动数,实现了提高调度公平性、优化调度延误时间、减少航班改变位置架次的多目标优化.将模糊自修正多目标粒子群算法(FS-MOPSO)应用于模型进行求解计算,并对上海多机场终端区航班调度进行仿真模拟,结果表明:两机场的30架进场航班调度延误时间较传统先到先服务方案减少22.53%;各航空公司航班改变位置架次偏差值较单一以延误最优遗传算法仿真结果降低26.31%.
基金Project(51075289) supported by the National Natural Science Foundation of ChinaProject(20122014) supported by the Doctor Foundation of Taiyuan University of Science and Technology,China
文摘A new intelligent anti-swing control scheme,which combined fuzzy neural network(FNN) and sliding mode control(SMC) with particle swarm optimization(PSO),was presented for bridge crane.The outputs of three fuzzy neural networks were used to approach the uncertainties of the positioning subsystem,lifting-rope subsystem and anti-swing subsystem.Then,the parameters of the controller were optimized with PSO to enable the system to have good dynamic performances.During the process of high-speed load hoisting and dropping,this method can not only realize the accurate position of the trolley and eliminate the sway of the load in spite of existing uncertainties,and the maximum swing angle is only ±0.1 rad,but also completely eliminate the chattering of conventional sliding mode control and improve the robustness of system.The simulation results show the correctness and validity of this method.
