Aircraft Approach Control With Stage-Based Trajectory Sequencing
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Solution Overview
Problem
Current air traffic management systems are prone to errors due to high workload and fatigue, and existing methods for aircraft approach management do not provide real-time guarantees or optimal solutions for complex air traffic control scenarios, especially at busy airports, leading to potential accidents and inefficiencies.
Innovation Solution
A system and method using dynamic programming and reachability analysis to autonomously design safe aircraft trajectories within a terminal maneuvering area, optimizing performance metrics under constraints of dynamics, actuation limitations, and safety requirements, while minimizing deviations from desired landing times and reducing communication needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual air traffic control procedures are used, then flexibility in handling diverse air traffic scenarios is maintained, but human error increases due to high workload and fatigue
Solution Approach 1:
The system enables autonomous aircraft approach management where the control system independently solves optimal control problems and determines landing sequences without continuous human intervention. The system serves itself by automatically monitoring aircraft states, computing trajectories, and adjusting control parameters based on real-time data from multiple sensors and communication systems.
2Productivity
If dynamic programming methods are used for determining landing sequences, then optimal sequencing is achieved, but comprehensive optimization of multiple air traffic control aspects is not provided
Solution Approach 1:
The control system performs multiple functions simultaneously: it determines optimal landing sequences, computes safe trajectories, monitors separation constraints, manages communication between aircraft and controllers, and optimizes multiple performance metrics including fuel efficiency, time to landing, and safety margins. This multi-functional approach replaces multiple specialized systems with a single unified control architecture.
3Productivity
If mixed-integer programming and dynamic programming are used for control problems, then theoretical optimality is achieved, but real-time guarantees are not provided for busy airports
Solution Approach 1:
The control problem is segmented into discrete stages corresponding to different phases of aircraft approach and landing. The terminal maneuvering area is divided into multiple zones with specific separation requirements for each zone. This segmentation allows the system to compute optimal control policies for each segment independently and combine them, reducing overall computational complexity while maintaining real-time performance guarantees.
4Speed
If heuristic solutions are used for aircraft control, then computational speed is improved, but the solution set is limited to specific maneuvers only
Solution Approach 1:
The control system dynamically adapts the set of allowable maneuvers based on current aircraft states, weather conditions, traffic density, and separation requirements. Rather than being restricted to a fixed set of heuristic maneuvers, the system can generate and execute diverse trajectories including holding patterns, vectoring maneuvers, and direct approaches, selecting from an expanded maneuver repertoire as computational resources and situational requirements dictate.
Data Source
AI summary
The present disclosure provides a system and a method for controlling an aircraft within a terminal maneuvering area (TMA) of an airport in the presence of multiple other aircraft. The method includes solving an optimal control problem subject to constraints maintaining a pre-determined separation of the aircraft from the other aircraft in the TMA to determine a state trajectory of the aircraft indexed on a predetermined sequence of TMA stages of the aircraft approaching a merging point in the TMA. The state trajectory of the aircraft is a sequence of states having a one-to-one correspondence with the sequence of TMA stages. The state of the aircraft includes a time state variable indicative of a time remaining for reaching the merging point. The aircraft is then controlled on the basis of an optimal state trajectory that is determined using the state trajectory.