Model Predictive Control of Flight Arrival Interval

Abstract:

In order to achieve the automation of Air Traffic Control (ATC), use system to identify the controlled model of flights arrival process which has been already built, using Model Predictive Control (MPC) of the dynamic matrix contro1 (DMC) to control the ATC process. According to DMC algorithm and the features of ATC, the design parameters of this system can be determined by a lot of simulations. It proves that the system design and parameters selection make the system has the required performance and the robustness even if the parameters be changed in a wide range. The experiment on the ATC Simulation System proves that the MPC method is available, conclusion of the study provides a new idea and method for the engineering implementation of the automation of flights arrival process control and some improvement of airspace utilization.

• Periodical: Advanced Materials Research (Volumes 503-504)
• Pages: 1375-1380
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.503-504.1375
• Online since: April 2012
• Authors: Da Peng Yang, Ping Bo Sun, Ke Qiang Hua
• Keywords: Minimum Variance Control, Model Predictive Control (MPC), Robustness

References

[1] Agogino, Adrian K. Distributed optimization for automating air traffic flow management [C]; Reston; American Institute of Aeronautics and Astronautics Inc., (2008).

[2] Zemrowski, K.M. Impacts of Increasing Reliance on Automation in Air Traffic Control Systems [C]. Piscataway, Inst. of Elec. and Elec. Eng. Computer Society, (2008).

DOI: 10.1109/systems.2008.4518987

[3] Wang Haidong, Sun Shuguang, Hua Keqiang. The Application of Fuzzy Petri Net in Sort of Aircraft Approach [J]. Journal of System Simulation , 2007. 19(18): 4298-4301.

[4] Hua Keqiang, Wang Xiujuan. The Research of Robustness Control in Flight Arrival Control [J]. Computer Engineering and Applications, 2010. 46(4): 240-242.

[5] Hua Keqiang, Wang XiuJuan, Zhao ShiWei. Modeling and Control in Flights Arrival Process [J]. Control Engineering, 2009. 16(5): 551-554.

[6] Yuxian, Z. Min, L. Robust model predictive control for uncertain discrete-time system with both states and input delays [C]. Control and Decision Conference, (2008).

DOI: 10.1109/ccdc.2008.4597315

[7] Qian Ji-Xin, and so on. Predictive Control, [M]. Beijing Chemical Industry Press，(2007).

[8] Liu Fuchun, Zhao Jun, Qian Jixin. Research on multivariable constrained fast dynamic matrix control algorithm [J]. Journal of Zhejiang University (Engineering Science), 2005, 35(9): 672-675.

[9] Sun Qing-Lin, Lin Xia. Predictive Functional Control Based on Incremental Weighted [J]. Control Engineering, 2006. 13 (1): 52-54.

[10] Qi Wei-Gui, Zhu, Xue-Li. DMC modified algorithm based on time series prediction principle [J]. Journal of Harbin Institute of Technology (New Series), 2002, 9(4): 327-331.