TRANSIT: Towards a Robust Airport Decision Support System for Intelligent Taxiing
Lead Research Organisation:
University of Sheffield
Department Name: Automatic Control and Systems Eng
Abstract
Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.
Organisations
Publications
Mahfouf M
(2021)
Searching & Generating Discrete-Event Systems
Obajemu O
(2021)
An interpretable machine learning based approach for process to areal surface metrology informatics
in Surface Topography: Metrology and Properties
Obajemu O
(2021)
Real-Time Four-Dimensional Trajectory Generation Based on Gain-Scheduling Control and a High-Fidelity Aircraft Model
in Engineering
Obajemu, O
(2017)
A Type-2 Fuzzy Modelling Framework for Aircraft Taxi-Time Prediction
Papananias M
(2022)
A Bayesian information fusion approach for end product quality estimation using machine learning and on-machine probing
in Journal of Manufacturing Processes
Papananias M
(2020)
Development of a New Machine Learning-based Informatics System for Product Health Monitoring
in Procedia CIRP
Papananias M
(2020)
Inspection by exception: A new machine learning-based approach for multistage manufacturing
in Applied Soft Computing
Wang X
(2021)
Aircraft taxi time prediction: Feature importance and their implications
in Transportation Research Part C: Emerging Technologies
Description | Hitherto, we have been able to: 1. model the complex dynamics associated with several types of aircrafts; 2. devised control algorithms for optimal taxiing around airports for speed and heading; 3. we have partially been able to include fuel consumption and CO2 emissions as constraints to the controls. 4. We have been able to validate the MATLAB-SIMULINK model relating to the BOEING 747 Aircraft using one type of simulator at Cranfield University and we are in the process of transferring the MATLAB-SIMULINK model hence developed to the real-time simulator at Cranfield University to be tested by pilots using Airport maps for taxiing schedules. 5. We have been able to conduct further extensive tests at Cranfield before hardware problems occurred and we have, as a result, been able to validate our model using two airports data, namely Manchester Airport and Hong-Kong Airport. In particular, we have been able to extend our BOEING 747 model to account for the slope terrain of Manchester Airport. It is worth noting that since the last submission in 2019, we have had one Conference Paper accepted at the 2020 IEEE Aerospace Conference 14 March 2020 in the USA for which this submission will be one day late. Furthermore, we have submitted a Paper to IEEE Transactions on Transportation Systems (2020), for which we are awaiting the outcome. |
Exploitation Route | Our findings in relation to modelling aircrafts for taxiing in airports, including the optimal control of engine speed and heading will be beneficial to other researchers who wish to apply our associated protocols in airports of their choice around the world. Furthermore, our algorithms take into account fuel consumption as well CO2 emissions will help save money and protect the environment. |
Sectors | Aerospace, Defence and Marine,Digital/Communication/Information Technologies (including Software),Energy,Transport |
URL | http://www.transitproject.co.uk |
Description | CMMI-EPSRC - Right First Time Manufacture of Pharmaceuticals (RiFTMaP) |
Amount | £1,543,632 (GBP) |
Funding ID | EP/V034723/1 |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 09/2021 |
End | 08/2024 |
Description | Future Advanced Metrology Hub |
Amount | £10,306,413 (GBP) |
Funding ID | EP/P006930/1 |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 03/2017 |
End | 02/2024 |