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A Data-Driven Model Predictive Control Framework for Multi-Aircraft TMA Routing Under Travel Time Uncertainty

Yi Zhang, Yushen Long, Liping Huang, Yicheng Zhang, Sheng Zhang, Yifang Yin

Abstract

This paper presents a closed-loop framework for conflict-free routing and scheduling of multi-aircraft in Terminal Manoeuvring Areas (TMA), aimed at reducing congestion and enhancing landing efficiency. Leveraging data-driven arrival inputs (either historical or predicted), we formulate a mixed-integer optimization model for real-time control, incorporating an extended TMA network spanning a 50-nautical-mile radius around Changi Airport. The model enforces safety separation, speed adjustments, and holding time constraints while maximizing runway throughput. A rolling-horizon Model Predictive Control (MPC) strategy enables closed-loop integration with a traffic simulator, dynamically updating commands based on real-time system states and predictions. Computational efficiency is validated across diverse traffic scenarios, demonstrating a 7-fold reduction in computation time during peak congestion compared to onetime optimization, using Singapore ADS-B dataset. Monte Carlo simulations under travel time disturbances further confirm the framework's robustness. Results highlight the approach's operational resilience and computational scalability, offering actionable decision support for Air Traffic Controller Officers (ATCOs) through real-time optimization and adaptive replanning.

A Data-Driven Model Predictive Control Framework for Multi-Aircraft TMA Routing Under Travel Time Uncertainty

Abstract

This paper presents a closed-loop framework for conflict-free routing and scheduling of multi-aircraft in Terminal Manoeuvring Areas (TMA), aimed at reducing congestion and enhancing landing efficiency. Leveraging data-driven arrival inputs (either historical or predicted), we formulate a mixed-integer optimization model for real-time control, incorporating an extended TMA network spanning a 50-nautical-mile radius around Changi Airport. The model enforces safety separation, speed adjustments, and holding time constraints while maximizing runway throughput. A rolling-horizon Model Predictive Control (MPC) strategy enables closed-loop integration with a traffic simulator, dynamically updating commands based on real-time system states and predictions. Computational efficiency is validated across diverse traffic scenarios, demonstrating a 7-fold reduction in computation time during peak congestion compared to onetime optimization, using Singapore ADS-B dataset. Monte Carlo simulations under travel time disturbances further confirm the framework's robustness. Results highlight the approach's operational resilience and computational scalability, offering actionable decision support for Air Traffic Controller Officers (ATCOs) through real-time optimization and adaptive replanning.

Paper Structure

This paper contains 18 sections, 14 equations, 10 figures, 3 tables.

Table of Contents

  1. Introduction
  2. System Framework
  3. Data analysis on aircraft TMA boundary arrival time
  4. Optimization controller formulation
  5. Source and sink constraints
  6. Route conservation law
  7. Arrival and departure constraints
  8. Safety gap constraints
  9. Rear-end constraints
  10. Objective
  11. Closed-loop Formulation
  12. Nominal Case
  13. Disturbance Case
  14. Experiment Results
  15. Computational Complexity
  16. ...and 3 more sections

Figures (10)

  • Figure 1: Schematic of a closed-loop system framework
  • Figure 2: Trajectory Data: left panel (within 200 nautical mile), right panel (within 50 nautical mile)
  • Figure 3: Data analysis on travel time distribution
  • Figure 4: Prediction on TMA boundary arrival time
  • Figure 5: TMA Runway 20 STARs (Captured from AIP 2022)
  • ...and 5 more figures