Conflict-free trajectory planning based on the model predictive control theory

• Autorzy: Han Yun-xiang , Huang Xiao-qoing

Identyfikatory

DOI

10.5604/08669546.1203205

• Języki publikacji: EN

Abstrakty

EN

Model Predictive Control (MPC) is a model-based control method based on a receding horizon approach and online optimization. A key advantage of MPC is that it can accommodate constraints on the inputs and outputs. This paper proposes a max-plus general modeling framework adapted to the robust optimal control of air traffic flow in the airspace. It is shown that the problem can be posed as the control of queues with safety separation-dependent service rate. We extend MPC to a class of discrete-event system that can be described by models that are linear in the max-plus algebra with noise or modeling errors. Regarding the single aircraft as a batch, the relationships between input variables, state variables and output variable are established. We discuss some key properties of the system model and indicate how these properties can be used to analyze the behavior of air traffic flow. The model predictive control design problems are defined for this type of discrete event system to help prepare the airspace for various robust regulation needs and we give some extensions of the air traffic max-plus linear systems.

Słowa kluczowe

EN

civil aviation; air transportation; aircraft; air traffic control; separation; trajectories; optimization; model predictive control

PL

lotnictwo cywilne; transport powietrzny; samolot; kontrola ruchu lotniczego; separacja; trajektorie; optymalizacja; sterowanie predykcyjne

• Wydawca: Warsaw University of Technology, Faculty of Transport
• Czasopismo: Archives of Transport
• Rocznik: 2016
• Tom: Vol. 37, iss. 1
• Strony: 77--85
• Opis fizyczny: Bibliogr. 27 poz., rys., tab.

Twórcy

autor

Han Yun-xiang

• Jiangsu University of Technology, School of automobile and traffic engineering, Changzhou, P.R China

autor

Huang Xiao-qoing

• Jiangsu University of Technology, School of business, Changzhou, P.R China

Bibliografia

• [1] SWENSON, H., BARHYDT, R., LANDIS, M., 2006. Next Generation Air Transportation System (NGATS) Air Traffic Management (ATM) - Airspace Project. Technical Report, Washington: NASA.
• [2] DLUGI, O., et al., 2007. SESAR D3 ATM Target Concept. Technical Report, EUROCONTROL, Montreal.
• [3] SCHUSTER, W., OCHIENG, W., 2014. Performance requirements of future Trajectory Prediction and Conflict Detection and Resolution tools within SESAR and NextGen: Framework for the derivation and discussion. Journal of Air Transport Management, 35, pp. 92-101.
• [4] RUIZ, S., PIERA, M. A., NOSEDAL, J., RANIERI, A., 2014. Strategic de-confliction in the presence of a large number of 4D trajectories using a causal modeling approach. Transportation Research Part C: Emerging Technologies, 39, pp. 129-147.
• [5] KUCHAR, J. K., YANG, L. C., 2000. A review of conflict detection and resolution modeling methods. IEEE Transactions on Intelligent Transportation Systems, 1(4), 179-189.
• [6] BARNIER, N., ALLIGNOL, C., 2009. 4D-trajectory deconfliction through departure time adjustment. In ATM 2009, 8th USA/Europe Air Traffic Management Research and Development Seminar. Jun 2009, Napa, United States.
• [7] BARNIER, N., ALLIGNOL, C., 2011. Combining flight level allocation with ground holding to optimize 4D-deconfliction. In ATM 2011, 9th USA/Europe Air Traffic Management Research and Development Seminar. Jun 2011, Berlin, Germany.
• [8] OMER, J., 2015. A space-discretized mixed-integer linear model for air-conflict resolution with speed and heading maneuvers. Computers & Operations Research, 58, 75-86.
• [9] DURAND, N., ALLIOT, J. M., NOAILLES, J., 1996. Automatic aircraft conflict resolution using genetic algorithms. In Proceedings of the 1996 ACM symposium on Applied Computing (pp. 289-298). Feb 1996, Philadelphia, United States.
• [10] PALLOTTINO, L., FERON, E. M., BICCHI, A., 2002. Conflict resolution problems for air traffic management systems solved with mixed integer programming. IEEE Transactions on Intelligent Transportation Systems, 3(1), pp. 3-11.
• [11] HU, J., PRANDINI, M., SASTRY, S., 2002. Optimal coordinated maneuvers for three-dimensional aircraft conflict resolution. Journal of Guidance, Control, and Dynamics, 25(5), pp. 888-900.
• [12] RAGHUNATHAN, A. U., GOPAL, V., SUBRAMANIAN, D., BIEGLER, L. T., SAMAD, T., 2003. 3D conflict resolution of systems. IEEE Transactions on Automatic Control, 43(4), pp. 509-521.
• [13] CLEMENTS, J. C., 1999. The optimal control of collision avoidance trajectories in air traffic management. Transportation Research Part B: Methodological, 33(4), pp. 265-280.
• [14] FRIEDMAN, M. F., 1991. Decision analysis and optimality in air traffic control conflict resolution: II. Optimal heading (vectoring) control in a linear planar configuration. Transportation Research Part B: Methodological, 25(1), pp. 39-53.
• [15] FRIEDMAN, M. F., 1988. Decision analysis and optimality in air traffic control conflict resolution I. optimal timing of speed control in a linear planar configuration. Transportation Research Part B: Methodological, 22(3), pp. 207-216.
• [16] VELA, A. E., SOLAK, S., CLARKE, J. P. B., SINGHOSE, W. E., BARNES, E. R., JOHNSON, E. L., 2010. Near real-time fuel-optimal en route conflict resolution. IEEE Transactions on Intelligent Transportation Systems, 11(4), pp. 826-837.
• [17] MATSUNO, Y., TSUCHIYA, T., WEI, J., HWANG, I., MATAYOSHI, N., 2015. Stochastic optimal control for aircraft conflict resolution under wind uncertainty. Aerospace Science and Technology, 43, pp. 77-88.
• [18] ALLIOT, J. M., DURAND, N., GRANGER, G., 2000. FACES: a Free flight Autonomous and Coordinated Embarked Solver, Air Traffic Control Quarterly, 8(1), pp. 109-130.
• [19] TOMLIN, C., PAPPAS, G. J., SASTRY, S., 1998. Conflict resolution for air traffic management: A study in multiagent hybrid systems. IEEE Transactions on Automatic Control, 43(4), pp. 509-521.
• [20] TOMLIN, C. J., LYGEROS, J., SASTRY, S. S., 2000. A game theoretic approach to controller design for hybrid systems. Proceedings of the IEEE, 88(7), pp. 949-970.
• [21] TOMLIN, C., MITCHELL, I., GHOSH, R., 2001. Safety verification of conflict resolution manoeuvres. IEEE Transactions on Intelligent Transportation Systems, 2(2), pp. 110-120.
• [22] GOVERDE, R. M., BOVY, P. H., OLSDER, G. J., 1999. The max-plus algebra approach to transportation problems. World Transport Research: Selected Proceedings of the 8th World Conference on Transport Research, 1999, pp.377-390.
• [23] OLSDER, G. J., 1989. Applications of the theory of stochastic discrete event systems to array processors and scheduling in public transportation. Proceedings of the 28th IEEE Conference on Decision and Control (pp.2012-2017).
• [24] OLSDER, G. J., 1993. Max algebra approach to discrete event systems. IEE Colloquium on Discrete Event Systems: A New Challenge for Intelligent Control Systems (pp.1-6).
• [25] COHEN, G., GAUBERT, S., QUADRAT, J. P., 1999. Max-plus algebra and system theory: where we are and where to go now. Annual reviews in control, 23, pp. 207-219.
• [26] VAN DEN BOOM, T. J., DE SCHUTTER, B., 2002. Model predictive control for perturbed max-plus-linear systems. Systems & Control Letters, 45(1), pp. 21-33.
• [27] DE SCHUTTER, B., VAN DEN BOOM, T., 2001. Model predictive control for max-plus-linear discrete event systems. Automatica, 37(7), pp. 1049-1056.

Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.