为了解决对机场实际运营中存在的共用同一中间机坪滑行道的航空器相对推出型冲突问题,文中根据时间有色Petri网的相关理论,分析了导致机场的运行效率降低的航班的推出问题,结合成都双流国际机场的相对推出型廊桥,提出了基于时间有色Petr...为了解决对机场实际运营中存在的共用同一中间机坪滑行道的航空器相对推出型冲突问题,文中根据时间有色Petri网的相关理论,分析了导致机场的运行效率降低的航班的推出问题,结合成都双流国际机场的相对推出型廊桥,提出了基于时间有色Petri网(Timed-Colored Petri Net,TCPN)的复杂情况下航空器推出模型。依据此模型将航空器从滑行道到停入机位的全过程进行模拟和说明。最后结合TCPN仿真软件CPN Tools中的状态空间分析法,对模型的可行性进行分析,对保障航班推出过程的运行安全、提高繁忙机场的运行效率具有重要的现实意义。展开更多
The goal of this paper is to propose a unique control method that permits the evolution of both timed continuous Petri net （TCPN） and T-timed discrete Petri net （T-TDPN） from an initial state to a desired one. Mod...The goal of this paper is to propose a unique control method that permits the evolution of both timed continuous Petri net （TCPN） and T-timed discrete Petri net （T-TDPN） from an initial state to a desired one. Model predictive control （MPC） is a robust control scheme against perturbation and a consistent real-time constraints method. Hence, the proposed approach is studied using the MPC. However, the computational complexity may prevent the use of the MPC for large systems and for large prediction horizons. Then, the proposed approach provides some new techniques in order to reduce the high computational complexity; among them one is taking constant control actions during the prediction.展开更多
文摘为了解决对机场实际运营中存在的共用同一中间机坪滑行道的航空器相对推出型冲突问题,文中根据时间有色Petri网的相关理论,分析了导致机场的运行效率降低的航班的推出问题,结合成都双流国际机场的相对推出型廊桥,提出了基于时间有色Petri网(Timed-Colored Petri Net,TCPN)的复杂情况下航空器推出模型。依据此模型将航空器从滑行道到停入机位的全过程进行模拟和说明。最后结合TCPN仿真软件CPN Tools中的状态空间分析法,对模型的可行性进行分析,对保障航班推出过程的运行安全、提高繁忙机场的运行效率具有重要的现实意义。
基金supported by the region Haute-Normandie Project(Nos.CPER-SER-DDSMRI 2013,2014 and CPER-SER-SEL 2015)
文摘The goal of this paper is to propose a unique control method that permits the evolution of both timed continuous Petri net （TCPN） and T-timed discrete Petri net （T-TDPN） from an initial state to a desired one. Model predictive control （MPC） is a robust control scheme against perturbation and a consistent real-time constraints method. Hence, the proposed approach is studied using the MPC. However, the computational complexity may prevent the use of the MPC for large systems and for large prediction horizons. Then, the proposed approach provides some new techniques in order to reduce the high computational complexity; among them one is taking constant control actions during the prediction.
