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

TL;DR

This paper introduces a data-driven, real-time Model Predictive Control framework for conflict-free multi-aircraft routing in TMA, improving efficiency and robustness under travel time uncertainties using adaptive optimization and simulation validation.

Contribution

It develops a novel closed-loop MPC-based routing model that integrates real-time data, enhancing operational resilience and computational efficiency in complex TMA environments.

Findings

7-fold reduction in computation time during peak congestion

Robustness confirmed through Monte Carlo simulations under travel time disturbances

Framework enables real-time adaptive replanning for air traffic control

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.