AI for smart spacecraft control (Ref: NUDATA24/EE/MPEE/WICKS)


   Faculty of Engineering and Environment

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  Prof Robert Wicks  No more applications being accepted  Competition Funded PhD Project (Students Worldwide)

About the Project

Overview of the CDT

This project is being offered as part of the STFC Centre for Doctoral Training in Data Intensive Science, called NUdata, which is a collaboration between Northumbria and Newcastle Universities, STFC, and a portfolio of over 40 industrial partners, including SMEs, large/multinational companies, Government and not-for profit organisations, and international humanitarian organisations. Please visit https://research.northumbria.ac.uk/nudata/ for full information.

Project description

Satellite mega-constellations are planned for many different applications, however they all have one thing in common, there must be a way to control the very large number of satellites automatically to maintain safe orbits and close formation flying. The traditional way of managing satellite control is to have a large team of people in a ‘mission control’ monitoring each satellite as it operates and testing every command before it is sent to make sure that the satellite will respond as intended. This is not possible when there are many tens to even thousands of satellites operating as a swarm. Insead, control processes must be automated and the more of these processes that can be managed internally by the satellite itself, the better.

This project is about the use of Physics Informed Neural Networks (PINNs) to encode the laws of gravity and motion together with a limited data input, for example from all-sky cameras on satellites or the ground, to simulate automated orbit prediction and then spacecraft control to maintain a particular formation of satellites. The objective is to make a PINN that can forecast manouevres needed in advance and then implement it in as efficient a way as possible on a realistic space-grade processor so that the process could be performed onboard a satellite. This technique must be compared to existing command and control processes such as Kalman filters.

This project will particularly suit someone with an interest in control systems, AI and image analysis. The project could be taken down a more theoretical route by a candidate with a willingness to use mathematics, as the laws of motion and gravity will need to be encoded correctly; or applicants with more interest in low-level programming of processors could focus on deploying the system onto realistic hardware. Image analysis will be needed to find the tracks of satellites and so techniques like de-noising,

Academic Enquiries

This project is supervised by Professor Robert Wicks. For informal queries, please contact [Email Address Removed]. For all other enquiries relating to eligibility or application process please contact Admissions at [Email Address Removed]. 

Recruitment Event

You will join a strong and supportive research team. To help better understand the aims of the CDT and to meet the PhD supervisors, we are hosting a day-long event on campus on Monday 15th January 2024. At that event, there will be an opportunity to discuss your research ideas, meet potential PhD supervisors, as well as hear from speakers from a variety of backgrounds (academia, industry, government, charity) discussing both STFC and data science as well as their personal paths and backgrounds. Click here for details.

Eligibility Requirements:

  • Academic excellence i.e. 2:1 (or equivalent GPA from non-UK universities with preference for 1st class honours); or a Masters (preference for Merit or above); or APEL evidence of substantial practitioner achievement.
  • Appropriate IELTS score, if required.
  • Applicants cannot apply if they are already a PhD holder or if currently engaged in Doctoral study at Northumbria or elsewhere.

To be classed as a Home student, candidates must:

  • Be a UK National (meeting residency requirements), or
  • have settled status, or
  • have pre-settled status (meeting residency requirements), or
  • have indefinite leave to remain or enter.

If a candidate does not meet the criteria above, they would be classed as an International student. 

Applicants will need to be in the UK and fully enrolled before stipend payments can commence and be aware of the following additional costs that may be incurred, as these are not covered by the studentship.

  • Immigration Health Surcharge https://www.gov.uk/healthcare-immigration-application
  • If you need to apply for a Student Visa to enter the UK, please refer to https://www.gov.uk/student-visa. It is important that you read this information carefully as it is your responsibility to ensure that you hold the correct funds required for your visa application, otherwise your visa may be refused.
  • Costs associated with English Language requirements which may be required for students not having completed a first degree in English, will not be paid by the University.

For further details on how to apply see

https://www.northumbria.ac.uk/research/postgraduate-research-degrees/how-to-apply/  

You must include the relevant advert reference/studentship code (e.g. NUDATA24/…) in your application.

If you are interested in more than one of the Northumbria-hosted NUdata research projects, then you can say this in the cover letter of your application and you can rank all the projects you are interested in (e.g. first choice, second choice, third choice). You are strongly encouraged to do this, since some projects are more popular than others. You only need to submit one application even if you are interested in multiple projects (we recommend you submit your application to your first choice).

Deadline for applications : 31st January 2024

Start date of course :  23rd September 2024

Northumbria University is committed to creating an inclusive culture where we take pride in, and value, the diversity of our postgraduate research students. We encourage and welcome applications from all members of the community. The University holds a bronze Athena Swan award in recognition of our commitment to advancing gender equality, we are a Disability Confident Leader, a member of the Race Equality Charter and are participating in the Stonewall Diversity Champion Programme. We also hold the HR Excellence in Research award for implementing the concordat supporting the career Development of Researchers and are members of the Euraxess initiative to deliver information and support to professional researchers.

Computer Science (8) Engineering (12) Mathematics (25) Physics (29)

Funding Notes

The 4-year studentship is available to Home and international (including EU) students and includes a full stipend at UKRI rates (for 2023/24 full-time study this was £18,622 per year) and full tuition fees. Studentships are also available for applicants who wish to study on a part-time basis in combination with work or personal responsibilities.

References

M. Raissi, P. Perdikaris, G.E. Karniadakis, Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations, Journal of Computational Physics,
Volume 378, 2019, 686-707, https://doi.org/10.1016/j.jcp.2018.10.045.
Physics-Informed Neural Networks for Optimal Planar Orbit Transfers
E. Schiassi, A. D’Ambrosio, K. Drozd, F. Curti, and R. Furfaro
Journal of Spacecraft and Rockets 2022 59:3, 834-849

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