Project description

This focus of this project is randomized optimization methods applied to agile satellite operations. Agile satellite operations can be defined as satellite attitude maneuvers that rapidly takes advantage of the full three-axis control capabilities of the satellite. Satellite operations in themselves include more than just the attitude control problem: there is also downlink capacity, energy constraints, orbital constraints, and pointing requirements, just to select a few examples. Randomized optimization methods are different from the classical optimization methods used in TTK4135 Optimization and Control: they do not require computation of derivatives and can for this reason be more closely associated with machine learning/AI methods. Examples of such methods are genetic algorithms, which is a subset of evolutionary algorithms. The goal when using such methods is that the system can overcome the issues of local minima that classical methods (among other things), which would be beneficial for this application.

Impact of this project

Space technology plays a critical role in achieving 40% to 50% of the UN Sustainable Development Goals. The NTNU SmallSat Lab's HYPSO satellites, launched in 2022 and with a successor planned, utilize hyperspectral imagers to capture detailed information beyond the visible. This data allows us to detect and monitor water bodies like oceans, fjords, and lakes, including vital yet potentially harmful algae. The HYPSO satellites also contribute to climate change studies by imaging the Arctic region. Data from the HYPSO satellites play a role in achieving Climate Action, Preserving Life Below Water, and ensure access to Clean Water and Sanitation. The work in this project can make an impact beyond improving the gathering of HYPSO data as the objective is to enable more agile operations, a type of operations which can be beneficial to a range of satellites, in particular small to medium satellites.

Tasks in this project

• Conduct a comprehensive literature review on randomized optimization.
• Model the problem and implement the model in a suitable environment.
• Develop and investigate new methods suited for this problem .
• Implement and show how the new methods compare to the state-of-the-art /comparable methods.

Who we are looking for
We are seeking a highly motivated final year student in Cybernetics and Robotics with interest for control. Experience from subjects such as TTK4190 (Guidance, Navigation and Control of Vehicles) will be beneficial for the student in this project. The project will be adapted to the student's goals and background. 

How we work
The student will be part of the NTNU SmallSat lab, a lab which typically hosts 10-20 master's student per semester.

Supervisors:

Tor Arne Johansen (main supervisor, NTNU), Bjørn Andreas Kristiansen (NTNU).

For more information about the project or to show your interest, contact Bjørn Andreas Kristiansen at bjorn.a.kristiansen@ntnu.no.