2021, Volume 7, Issue 4
Engineering >> 2021, Volume 7, Issue 4 doi: 10.1016/j.eng.2021.01.009
Real-Time Four-Dimensional Trajectory Generation Based on Gain-Scheduling Control and a High-Fidelity Aircraft Model
a Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield S1 4DW, UK
b Business and Management Research Institute, University of Bedfordshire, Luton LU1 3JU, UK
c School of Engineering and Materials Science, Queen Mary University of London, London E1 4NS, UK
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Abstract
Aircraft ground movement plays a key role in improving airport efficiency, as it acts as a link to all other ground operations. Finding novel approaches to coordinate the movements of a fleet of aircraft at an airport in order to improve system resilience to disruptions with increasing autonomy is at the center of many key studies for airport airside operations. Moreover, autonomous taxiing is envisioned as a key component in future digitalized airports. However, state-of-the-art routing and scheduling algorithms for airport ground movements do not consider high-fidelity aircraft models at both the proactive and reactive planning phases. The majority of such algorithms do not actively seek to optimize fuel efficiency and reduce harmful greenhouse gas emissions. This paper proposes a new approach for generating efficient four-dimensional trajectories (4DTs) on the basis of a high-fidelity aircraft model and gainscheduling control strategy. Working in conjunction with a routing and scheduling algorithm that determines the taxi route, waypoints, and time deadlines, the proposed approach generates fuel-efficient 4DTs in real time, while respecting operational constraints. The proposed approach can be used in two contexts: ① as a reactive decision support tool to generate new trajectories that can resolve unprecedented events; and ② as an autopilot system for both partial and fully autonomous taxiing. The proposed methodology is realistic and simple to implement. Moreover, simulation studies show that the proposed approach is capable of providing an up to 11% reduction in the fuel consumed during the taxiing of a large Boeing 747 jumbo jet.
Keywords
Aircraft model ; Intelligent taxiing ; Optimization ; Four-dimensional trajectory
SupplementaryMaterials
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References
[ 1 ] Eurocontrol. Strategic guidance in support of the execution of the European ATM master plan. Report. Paris: European Organization for the Safety of Air Navigation; 2009.
[ 2 ] Concept of operations for the next generation air transportation system. Report. Frederick: Aircraft Owners and Pilots Association; 2006 Jul.
[ 3 ] Brooker P. SESAR and NextGen: investing in new paradigms. J Navig 2008;61 (2):195–208. link1
[ 4 ] Clare G, Richards A. Airport ground operations optimizer. In: Proceedings of the 8th Eurocontrol Innovative Research Workshop & Exhibition Proceedings; 2009 Dec 1–3; Paris, France; 2009.
[ 5 ] Chen J, Weiszer M, Stewart P, Shabani M. Toward a more realistic, costeffective, and greener ground movement through active routing part 1: optimal speed profile generation. IEEE Trans Intell Transp Syst 2015;17 (5):1196–209. link1
[ 6 ] Cook A, Tanner G, Cristóbal S, Zanin M. New perspectives for air transport performance. In: Proceedings of the 3rd SESAR Innovation Days; 2013 Nov 26– 28; Stockholm, Sweden; 2013.
[ 7 ] Haus S, Sendobry A, Urvoy C, Klingauf U. Control theoretic concept for intuitive guidance of pilots during taxiing. In: Proceedings of 2011 IEEE/AIAA 30th Digital Avionics Systems Conference; 2011 Oct 16–20; Seattle, WA, USA; 2011.
[ 8 ] Urvoy C, Drege C, Heusel S, Klingauf U. Concept for a human centered 4D surface guidance system. In: Proceedings of the 29th Conference of the European Association for Aviation Psychology; 2010 Sep 20; Budapest, Hungary; 2010. p. 77–82.
[ 9 ] Clare G, Richards AG. Optimization of taxiway routing and runway scheduling. IEEE Trans Intell Transp Syst 2011;12(4):1000–13. link1
[10] Li J, Gong M, Liang Z, Liu W, Tong Z, Yi L, et al. Departure scheduling and taxiway path planning under uncertainty. In: Proceedings of AIAA Aviation Forum; 2019 Jun 17–21; Dallas, TX, USA; 2019.
[11] Atkin J. On-line decision support for take-off runway scheduling at London Heathrow Airport [dissertation]. Nottingham: The University of Nottingham; 2008.
[12] Weiszer M, Chen J, Stewart P. A real-time active routing approach via a database for airport surface movement. Transp Res Part C Emerg Technol 2015;58:127–45. link1
[13] Wu C, Zhang W, Lu S, Tan Z, Xue F, Yang J. Train speed trajectory optimization with on-board energy storage device. IEEE Trans Intell Transp Syst 2019;20 (11):4092–102. link1
[14] Zhang T, Weiszer M, Chen J. The feasibility of follow-the-greens for 4- dimensional trajectory based airport ground movements. Transp Res Part C Emerg Technol 2020;116(11):102632. link1
[15] Khadilkar H, Balakrishnan H. Estimation of aircraft taxi fuel burn using flight data recorder archives. Transp Res Part D Transp Environ 2012;17 (7):532–7. link1
[16] Ross IM, Karpenko M. A review of pseudospectral optimal control: from theory to flight. Annu Rev Contr 2012;36(2):182–97. link1
[17] Raymer DP. Aircraft design: a conceptual approach. 2nd ed. Reston: American Institute of Aeronautics and Astronautics Inc.; 1992.
[18] Hanke CR. The simulation of a large jet transport aircraft. Volume 1: mathematical model. Report. Washington: National Aeronautics and Space Administration; 1971.
[19] Hanke CR, Nordwall DR. The simulation of a jumbo jet transport aircraft. Volume 2: modeling data. Report. Washington: National Aeronautics and Space Administration; 1970. link1
[20] Duke EL, Antoniewicz RF, Krambeer KD. Derivation and definition of a linear aircraft model. Report. Washington: National Aeronautics and Space Administration; 1988 Aug.
[21] Allerton D. Principles of flight simulation. Hoboken: John Wiley & Sons, Inc.; 2009. link1
[22] ICAO Committee on Aviation Environmental Protection Working Group. Aircraft Engine exhaust emissions databank. Report. Montreal: International Civil Aviation Organization; 1995.
[23] Obajemu O, Mahfouf M, Catto JW. A new fuzzy modeling framework for integrated risk prognosis and therapy of bladder cancer patients. IEEE Trans Fuzzy Syst 2018;26(3):1565–77. link1
[24] Obajemu O, Mahfouf M. A dirichlet process based type-1 and type-2 fuzzy modelling for systematic confidence bands prediction. IEEE Trans Fuzzy Syst 2019;27(9):1853–65. link1
[25] Obajemu O, Mahfouf M, Maiyar LM, He C, Allerton DJ, Chen J, et al. Fuzzy modelling of fuel consumptions and emissions for optimal navigation of a Boeing-747 aircraft. In: Proceedings of 2020 IEEE Aerospace Conference; 2020 Mar 7–14; Big Sky, MT, USA; 2020.
[26] Weiszer M, Burke EK, Chen J. Multi-objective routing and scheduling for airport ground movement. Transportation Res Part C Emerg Technol 2020;119:102734. link1
[27] Weiszer M, Chen J, Ravizza S, Atkin J, Stewart P. A heuristic approach to greener airport ground movement. In: Proceedings of IEEE Congress on Evolutionary Computation (CEC); 2014 Jul 6–11; Beijing, China; 2014. p. 3280–6.
[28] Chen J, Weiszer M, Locatelli G, Ravizza S, Atkin JA, Stewart P, et al. Toward a more realistic, cost-effective, and greener ground movement through active routing: a multiobjective shortest path approach. IEEE Trans Intell Transp Syst 2016;17(12):3524–40. link1
[29] Zhang T, Ding M, Zuo H, Chen J, Weiszer M, Qian X, et al. An online speed profile generation approach for efficient airport ground movement. Transp Res Part C Emerg Technol 2018;93:256–72. link1
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