Particle swarm-based trajectory and mission optimisation for LEO SmartSat applications

This thesis is the result of a collaboration between Politecnico di Torino and the Royal Melbourne Institute of Technology. This work aims to develop an automated algorithm to reorient small satellites and change their orbit to avoid debris collisions. In a context where it is essential to reduce costs this concept of a space telescope is presented. This thesis outlines an optimal strategy for trajectory and attitude reorientation for autonomous SmartSats. On-board autonomy allows to reduce the workload on ground-stations and will be mandatory in the next future where satellites will need to accomplish their mission autonomously. This satellite shall be able to optimise its attitude and its trajectory on-board and in real-time. A basic design of the satellite is firstly developed while an optimisation algorithm is used to obtain optimal control solutions for trajectory and attitude. Particle swarm optimisation, an evolutionary algorithm reproducing the natural behaviour of swarms, is used to generate a possible solution to control the satellite in the case of continuous low thrust where a polynomial parametrisation is used to model the control time history. For attitude optimisation an inverse dynamic approach is adopted; using polynomial Bézier curves to achieve a possible solution, PSO is used to meet the constraints and then to find the time history for control and angular velocities. Disturbance torques due to solar radiation pressure and atmospheric drag are considered in reorientation optimisation. Lastly, the calculation of a probability of collision is presented to move the satellite avoiding the collision with orbital debris in LEO orbits.