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

2025-11-19

AI Summary

To address conflict risks and low landing efficiency arising from uncertain aircraft arrival times in the Terminal Maneuvering Area (TMA), this paper proposes a closed-loop, data-driven Model Predictive Control (MPC) framework. The method integrates real-time ADS-B–driven arrival time prediction with receding-horizon mixed-integer optimization to jointly resolve conflicts, re-plan routes, and coordinate scheduling; it further incorporates an extended TMA network model and Monte Carlo–based robustness verification. Evaluated on Singapore’s real-world airspace, the framework achieves a 7× improvement in peak computational efficiency and significantly increases runway throughput while strictly maintaining safe separation minima. Its key innovation lies in the first integration of data-driven arrival time estimation into a closed-loop MPC architecture—thereby simultaneously ensuring real-time responsiveness, robustness against uncertainty, and scalability—providing a deployable solution for intelligent terminal air traffic management under operational uncertainty.

Technology Category

• Planning, Routing, and Scheduling: Model-Based Reasoning
• Multiagent Systems: Multiagent Planning
• Intelligent Robots: State Estimation

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Optimizing multi-aircraft routing in terminal areas under travel time uncertainty
• Enforcing safety separation while maximizing runway throughput efficiency
• Providing real-time conflict-free routing with computational scalability

Innovation

• Closed-loop MPC framework for conflict-free aircraft routing
• Mixed-integer optimization with data-driven arrival predictions
• Rolling-horizon control enabling real-time adaptive replanning