M.Sc Thesis

M.Sc StudentAvner Mouchnino
SubjectLow-Thrust Orbital Transfer with Mitigated Space-Debris
Collision Risk
DepartmentDepartment of Aerospace Engineering
Supervisor Professor Gurfil Pinchas
Full Thesis textFull thesis text - English Version


The intensive use of space produces huge amounts of space debris in orbit around the Earth. These space debris represent a real threat for active satellites and impact the reliability of the different space services. In this research, we investigate different methods to mitigate the collision risk with space debris using satellite maneuvers to bypass the obstacle or planning an orbit with minimal collision risk. The research considers collision avoidance with space debris moving on a known trajectory using impulsive or continuous thrust.

In the impulsive control, after identifying a risk for collision, the satellite adopts a responsive strategy to avoid the collision by producing velocity impulses to deviate from the orbit while remaining on a secured distance from the space debris. In this optimization problem we have to reduce the fuel consumption required for the maneuver i.e. collision avoidance maneuver with a minimal impulse. According to the mission characteristics, we will choose a collision avoidance maneuver that will take back the satellite to its initial orbit after bypassing the obstacle (bi-impulsive maneuver) or passing to a new orbit that is collision-free (single impulse maneuver). The optimization for bi-impulsive maneuvers is based on Lambert's problem. The single-impulse maneuver optimization searches for an orbit with a minimal deviation from the nominal one using the Gauss Variational Equations. The optimization for these two cases is performed by combining Genetic Algorithms with Pattern Search.

In continuous control, before starting the transfer orbit, the trajectory is planned in advance as a proactive strategy to avoid crossing high density debris zones. The optimal control is performed using the pseudospectral direct method that parameterizes the state and control variables. The optimization takes into account some non-recommended zones in addition to the minimum fuel consumption objective.