..... Research Directions .....

Distributed Scheduling Algorithm in Air Traffic Flow Management

  • An extended Eulerian-Lagrangian flow model is proposed to describe the network dynamics with possibly different aircraft types
  • A quadratic integer programming problem is formulated for delay reduction under constraints of limited link capacities along with possibility of flight rerouting and diversion
  • A distributed approach and a heuristic algorithm is developed to reduce the computational complexity

References

  1. Y. Zhang, R. Su, G.G.N. Sandamali, Y. Zhang and C.G. Cassandras. “A Hierarchical Approach for Air Traffic Routing and Scheduling 2017 IEEE 56th Annual Conference on Decision and Control (CDC), Melbourne, VIC, 2017, pp. 6277-6282.
  2. Y. Zhang, R. Su, G. G. N. Sandamali, Y. Zhang, C. G. Cassandras and L. Xie. A Hierarchical Heuristic Approach for Solving Air Traffic Scheduling and Routing Problem with a Novel Air Traffic Model, in IEEE Transactions on Intelligent Transportation Systems, doi: 10.1109/TITS.2018.2874235.
  3. Y. Zhang, R. Su, Q. Li, C. G. Cassandras, and L. Xie. “Distributed Flight Routing and Scheduling in Air Traffic Flow Management. 2016 IEEE 55th Conference on Decision and Control (CDC), Las Vegas, NV, 2016, pp. 1080-1085.
  4. Y. Zhang, R. Su, G.G.N. Sandamali, Y. Zhang and C.G. Cassandras. A Hierarchical Approach for Air Traffic Routing and Scheduling, IEEE 56th Annual Conference on Decision and Control (CDC), Melbourne, VIC, 2017, pp. 6277-6282.
  5. Y. Zhang, Q. Li, and R. Su. “Sector-based Distributed Scheduling Strategy in Air Traffic Flow Management. IFAC-PapersOnLine, Vol 49, No 3, 2016, pp. 365-370
  6. Q. Li, Y. Zhang, and R. Su. “A Flow-based Flight Scheduler for En-route Air Traffic Management. IFAC-PapersOnLine 49, Vol 49, No 3, 2016, pp. 353-358

Real-time Flight Scheduling for Optimal Air Traffic Flow Management

  • An ATFM model which takes the capacity uncertainty and demand uncertainty into account with a flight-by-flight structure.
  • Two phase ATFM framework addressing pre-tactical and tactical phases.
  • Reduction in violations/conflicts due to uncertainties of capacity and demand from the system.
  • Ensuring of the safety separation between flights through the entire flight durations, while complying with sector capacities.
  • A flight level assignment strategy based on optimization of fuel consumptions using Base of Aircraft Data (BADA).
  • Enhancement of the scalability through a flow based model, while still maintaining unique features in a flight-by-flight structure.
  • An efficient solution mechanism, which can be applied in solving realistic large scale ATFM problems.
  • A comprehensive simulation study, including realistic air traffic scenarios.

References

  1. G. G. N. Sandamali, R. Su and Y. Zhang. Flight Routing and Scheduling Under Departure and En-route Speed Uncertainty, in IEEE Transactions on Intelligent Transportation Systems, doi: 10.1109/TITS.2019.2907058.
  2. G. G. N. Sandamali, R. Su, and Y. Zhang, Short-term En route Air Traffic Flow Management Under Departure and Wind Uncertainty with a Heuristic and Greedy Solution Approach, 2019 American Control Conference (ACC), Philadelphia, USA, pp. 2121-2126, doi: 10.23919/ACC.2019.8814968
  3. G. G. N. Sandamali, R. Su, Y. Zhang, and Q. Li, “Flight Routing and Scheduling with Departure Uncertainties in Air Traffic Flow Management 2017 13th IEEE International Conference on Control & Automation (ICCA), Ohrid, 2017, pp. 301-306, doi: 10.1109/ICCA.2017.8003077..
  4. G. G. N. Sandamali, R. Su, and K. L. K. Sudheera, "Flow-based Flight Routing and Scheduling under Uncertainty", Submitted to 21st IFAC World Congress (Accepted), 2020.
  5. G. G. N. Sandamali, R. Su and Y. Zhang "A Safety-Aware Real-time Air Traffic Flow Management Model Under Demand and Capacity Uncertainties," IEEE Transactions on Intelligent Transportation Systems (Under major reveiw).
  6. G. G. N. Sandamali, R. Su, K. L. K. Sudheera, Y. Zhang and Y. Zhang "Two-stage Scalable Air Traffic Flow Management Model under Uncertainty," IEEE Transactions on Intelligent Transportation Systems (Under major review).

Flight Trajectories Generation

  • A model aims at optimizing flight plan for the traffic system, i.e. assign route and schedule for individual aircraft
  • With considerations of Dispatching constraint, Sequence constraint, Separation constraint, Capacity constraint Speed constraint

References

  1. Q. Li, Y. Zhang, and R. Su. A Flow-based Flight Scheduler for En-route Air Traffic Management. IFAC-PapersOnLine 49, Vol 49, No 3, 2016, pp. 353-358

Fixed time multi-party cluster consensus in Air Traffic Network

  • Intend to design a finite time consensus protocol for an air traffic network
  • Higher level control strategy will provide us routine and scheduling of the aircrafts
  • Different way points in a network we will have a fixed arrival time
  • The objective is to design a distributed fixed time multi-party consensus algorithm to support the routine and scheduling
  • Consensus will help to maintain a fixed safe separation while crossing the way points at specified time
  • Fixed time consensus will enable to design point graph to realize an air traffic network

References

  1. F. Adib Yaghmaie, K. Hengster-Movric, F. L. Lewis, R. Su and M. Sebek. H∞ output regulation of linear heterogeneous multiagent systems over switching graphs , in International Journal of Robust and Nonlinear Control, vol. 28, Issue. 13, pp. 3852-3870, April. 2018.
  2. S. Mondal, and R. Su. Multiparty Consensus of Multi Agent Systems using Fixed time Control, IEEE 57th Annual Conference on Decision and Control (CDC), USA, 2018, pp. 6192-6197.
  3. F. A. Yaghmaie, R. Su, F. L. Lewis, L. Xie. Multi-party consensus of linear heterogeneous multi-agent systems. in IEEE Transactions on Automatic Control, vol. 62, no. 11, pp. 5578-5589, Nov. 2017.
  4. S. Mondal, R. Su, and L. Xie. Heterogeneous consensus of higher order multi agent systems with mismatched uncertainties using sliding mode control. in International Journal of Robust and Nonlinear Control, vol. 27 No 13, pp. 2303-2320, Sep. 2017.
  5. S. Mondal, and R. Su. “Finite time tracking control of higher order nonlinear multi agent systems with actuator saturation. IFAC-PapersOnLine 49, Vol 49, No 3, 2016, pp. 165-170
  6. S. Mondal, and R. Su. Disturbance observer based consensus control for higher order multi-agent systems with mismatched uncertainties. 2016 American Control Conference (ACC), Boston, MA, 2016, pp. 2826-2831.

Sector or En-route Capacity Estimation

  • Air traffic capacity is estimated from AirTOp simulation data
  • Proposed methodology will be extended to the experimental flight data to predict the air traffic capacity
  • Developed the capacity model building algorithm in MATLAB

Simulation Package Design

  • Combined the ATFM, flight trajectories generation module with simulation platform
  • Developed based on AirTOp, a real-time air traffic simulator
 

..... Demos .....

Air Traffic Flow Management Simulation on AirTOp platform

Simulation shown during our presentation at CDC’16, Las Vegas, Dec 2016