Automated control of Earth observation missions does not only allow for a more efficient use of onboard resources but also improved quality on the scientific return and cost reductions in the operations. Current developments in space hardware computational capabilities allow the implementation of onboard optimisation algorithms that can use the state estimations available onboard to dynamically generate control sequences in adaptation to disturbances or unpredictable behaviour.

This study proposes an onboard target management software with the aim of observing a set of targets on Earth’s surface in an automated manner. The development must be carried out within the framework of AAC Hyperion’s integrated attitude determination and control system. The proposed solution is designed to autonomously manage observations and optimise reorientation manoeuvers to designated ground targets, reducing the need for continuous ground intervention and enhancing operational efficiency. The method proposed combines the solving of a Mixed-Integer Linear Programming (MILP) optimisation problem for the scheduling of the observations, while
using successive convex programming to perform optimal control for spacecraft constrained reorientation and target pointing in a real-time manner. These two primary components, together with another task for computing the target visibility windows, operate within a modular framework, facilitating communication with the existing ADCS architecture and making efficient use of onboard computational resources.

The MILP scheduling subproblem selects targets based on priority, visibility constraints, and the required slew times between observations. Initial results identified limitations in MILP performance, particularly as the number of targets or proximity constraints increased, where large computational loads exceeded real-time processing capabilities. Alternative scheduling strategies are suggested for highly complex or densely populated target scenarios. Convex optimisation is found to be a suitable method for the solving of highly constrained manoeuvres in real-time. The reformulation of non-linear systems and complex, combined with the use of successive convexification were used to efficiently solve the spacecraft attitude control problem. This method’s robustness and
capability to detect infeasibility were especially beneficial for the modular onboard software.

The research tested and validated the proposed algorithms using a software-based real-time simulation environment, allowing a preliminary assessment of the observation manager’s performance. On the one hand, the method used for approximating the slew rate was found innaccurate, worsening the performance of the convex optimisation due to its interdependency with the MILP optimisation. On the other hand, results showed that the convex optimization method was effective for achieving precise target orientations, but achieving target accuracy of 0.1° often required refinement through additional onboard controllers, such as a PID controllers, to maintain
target lock during observation. Additionally, the trajectory optimiser demonstrated robustness at an update rate of 0.2 seconds, allowing the spacecraft to realign to targets successfully despite potential deviations in spacecraft state. However, the limited memory and slower processing speed of the ADCS hardware present challenges in extending these methods to onboard use.

Based on the results, the study suggests the modification of the slew approximation, and the use of alternative methods to the MILP, such as dynamic programming, to improve scheduling efficiency in future work. Moreover, testing on the ADCS hardware processor through the use of HIL simulations is essential to faithfully reproduce the onboard performance for analysis. A shift to higher-performance onboard processors or offloading some computational tasks to external processors is recommended to fully support the demands of automated Earth observation operations. This thesis demonstrates the feasibility and potential of integrating advanced optimisation methods onboard Earth observation satellites, providing a foundation for future advancements in autonomous spacecraft operations and contributing to the field of real-time, dynamic scheduling, and control in space systems.