Recent years have seen an increased need for a lunar navigation service to support the large volume of missions planned for the coming decade. Supplying lunar navigation from the Earth suffers from limitations deriving from the long distance to the Moon. Thus, it must rely on a constellation of lunar navigation satellites. This thesis addresses the Orbit Determination (OD) of the satellites of a Lunar Navigation System (LNS) using range and Doppler observations from terrestrial stations. It provides a recommendation for the values of the design parameters of the OD system of a LNS to achieve the required OD accuracy and evaluates the impact of non-design factors such as the constellation's geometry and the observational noise. The parameters of the orbit propagation and the length of the estimation arc are identified as drivers of the OD accuracy. The relation between OD errors and navigation performance is also assessed.

Neglecting actuator dynamics in nonlinear control and control allocation can lead to performance degradation, especially when considering fast dynamic systems. This thesis provides a novel method to account for actuator dynamics in the control allocation solution, dynamic incremental nonlinear control allocation, or D-INCA. The incremental approach allows for the implementation of a first order discrete-time actuator dynamics model in the quadratic programming (QP) solver. This model is used to find the optimal command inputs in addition to the desired physical actuator deflections, hereby compensating for actuator dynamics delays. Whereas, the baseline incremental nonlinear control allocation (INCA) approach requires pseudo-control hedging of the outer loop reference to increase closed loop stability margins under actuator dynamics delays. To its advantage, D-INCA does not require feedback of higher order output derivatives than INCA and can be used with nonlinear non-control affine systems. Furthermore, with adaptive D-INCA, or AD-INCA, an actuator dynamics parameter estimator is introduced to adapt the actuator model online, minimizing actuator tracking errors after actuator failures. The proposed methods are applied to a fighter aircraft model with an over-actuated innovative control effectors suite and results are compared to the baseline INCA controller.

Reinforcement Learning applied to flight control has shown to have several benefits over classical, linear flight controllers, as it eliminates the need for gain scheduling and it could provide fault-tolerance. The application to civil aviation in practice, however, is non-existent as there are multiple safety concerns. This research demonstrates the evaluation of longitudinal Handling Qualities of the Soft Actor-Critic Deep Reinforcement Learning framework with the aim to translate the unpredictable black box of Reinforcement Learning into classical flight control terminology. The framework is applied to a pitch rate command system of a jet aircraft and shows robustness to off-nominal flight conditions, center of gravity shifts and biased sensor noise. Accurate tracking performance is achieved, while adhering to Level 1 longitudinal Handling Qualities for all conditions.

For this Thesis a weighted spherical harmonic analysis is performed on the intensity distribution of Io's hotspots to find potential long wavelength patterns. Unfortunately, no definitive conclusion can be drawn as to whether the primary signal (Loki Patera) is caused by a long wavelength signal originating from deep within Io, or whether it is caused by a local phenomenon. This is because the spectral power associated with the analysis does not just have significant power in one or a few degrees. Instead all harmonic degrees return a strong signal. This has been checked up to at least degree 30 which is well into the shorter wavelengths.

Comparing the weighted spherical harmonic analysis of Io’s hotspot intensity distribution to an unweighted spherical harmonic analysis of the hotspot distribution (so without intensities), the location of the primary signal(s) is different between the two analyses, but both still point towards the asthenospheric heating model or magma ocean heating model as the explanation for Io’s heat flow pattern.

On-orbit servicing and active debris removal missions are becoming increasingly important to limit the growth of space debris in Earth’s orbit. SmallSats offer a promising option for reducing the cost and risk of these missions, allowing for their expanded use on a wider scale. For these missions to be successful, SmallSats are required to operate at very-close ranges to a target object. Relative visual navigation at this distance still presents a hurdle that needs to be overcome prior to implementing these platforms. This work analyses the suitability of Simultaneous Localization and Mapping (SLAM) for navigation at very-close ranges and proposes a modified algorithm to increase performance. Characterization of this algorithm is performed against a custom generated lab-based image dataset simulating final approach to a satellite. Algorithm performance shows promising results for SLAM use in very-close range environments and presents a first step in the development of this SmallSat navigation technology.

The Flying-V aircraft could revolutionize commercial aviation, boasting a potential 25% increase in aerodynamic efficiency. Due to inherent design limitations regarding static stability, the need for a proper Flight Control System (FCS) is essential for the development of the aircraft. The concept of Hybrid Incremental Nonlinear Dynamic Inversion (INDI) was introduced to mitigate the insufficient stability margin encountered in existing sensor-based INDI systems due to sensor time delays, to achieve Level 1 Handling Qualities (HQ). Furthermore, the research introduces an exponential potential function-based command limiting Flight Envelope Protection (FEP) to enhance safety compared to the currently implemented linear-based FEP. The study compares and evaluates the effectiveness of the updated system under various flight conditions and parametric uncertainties. Results show improved stability margins and a safer FEP. However, additional research is required into actuator saturation and control allocation issues during the approach condition and to enhance fault tolerance.

Surface heat flux, which can be defined as the heat flowing out of the interior of a planetary body, provides important constraints on the present-day thermal state of the lunar interior. Measurements, performed in situ during the Apollo program, and from orbit by Chang’E 1, Chang’E 2, and by the Diviner radiometer instrument, indicate important lateral variations in surface heat flux on the Moon (~5-180 mW/m²). The differences between Apollo 15 and Apollo 17 measurements have been explained by the presence of an anomalous region, enriched in uranium (U), thorium (Th), and KREEP (Potassium, Rare Earth Elements, and Phosphorus) elements, located on the lunar nearside. Previous modeling efforts also identified crustal thickness and thermal conductivity variations as secondary causes for surface heat flux differences. However, detailed explanations for the remaining two estimates and their implications for the evolution of the Moon remain highly debated. Additionally, the structure and properties of this putative KREEP-rich layer have remained uncertain.

Therefore, this study proposes a new global geodynamic model of lunar thermal evolution that includes lateral variability in the distribution of radiogenics, crustal thickness, and thermal conductivity. The present setup is capable of simultaneously explaining the variability between the Apollo 15, Apollo 17, and Region 5 heat flux values, while also providing further constraints on the KREEP layer structure and lunar crustal properties. The research question addressed in this work is: What is the effect of crustal structure (radiogenics, thickness, and thermal conductivity distribution) on 3D thermal evolution models of the Moon?

Here, we model the interior dynamics and thermal evolution of the Moon after magma ocean solidification using the fluid solver GAIA. We investigate the abundance and distribution of radiogenics on the Moon, and how they shape the interior temperature distribution through time. Additionally, we account for a spatially variable crustal thickness, derived from gravity and topography data. We also include a laterally variable thermal conductivity model, derived from porosity data, which we constrain using the nearside-farside differential thermal state of lunar basins. We model and vary the extent of a putative KREEP layer underlying the PKT (Procellarum KREEP Terrane) region. We enrich the KREEP layer and crust in heat-producing elements compared to the mantle, simulating an asymmetrical distribution of radiogenics as an initial condition.

We find that measurably lower heat flux values at the lunar south pole compared to Apollo 15 and 17 require KREEP material to extend at least partly beneath Mare Serenitatis. In this case, the Apollo 15 measurement would be representative of the KREEP region average heat flux, while Apollo 17 would lie on its edge. On the other hand, a smaller KREEP region (<1200 km in diameter) would make the Apollo 17 location representative of non-KREEP terrane, and show heat flux comparable to south pole values. This is incompatible with estimates based on the Diviner Lunar Radiometer Experiment onboard Lunar Reconnaissance Orbiter, although uncertainties associated with these estimates are unclear. Heat flux measurements that will be performed by the NASA CLPS-CP12 mission at Schrödinger crater will provide key information to exclude one of these two scenarios, and thus potentially constrain the extent of the KREEP layer underneath the PKT region.

Our results also show that a laterally variable thermal conductivity helps reduce the interior temperatures, while maintaining surface heat flux distribution unchanged. We find an effective farside crustal conductivity of ~2 W/(mK) (comparable to that of compact anorthosite) to best match the differential basin relaxation constraints, implying negligible effect of a porous megaregolith layer on the thermal conductivity. In the KREEP region, we favour models with effective crustal conductivity below 2 W/(mK), suggesting that lunar volcanic basalts may have an even lower bulk conductivity than the 2.6-2.7 W/(mK) values considered here. This could be due to the porosity of lunar volcanic material, a more complex layering of basaltic eruptions, or the effect of the temperature and pressure dependence of thermal conductivity. Although our model setup is simplified, it provides novel insights on the distribution of radiogenics and crustal properties on the Moon, and shows the potential to further constrain the asymmetrical character of lunar evolution.

This study demonstrates an effective systematic control design procedure by applying H∞ Loop-Shaping with a structured controller on an agile aerospace vehicle with a focus on automation. The gain-scheduled implementation is additionally described and tested with non-linear simulations, including a realistic moving point-hit scenario with guidance. The imposed robustness and performance requirements are met for most linear design points and for the non-linear simulations. The resulting autopilot design procedure is deemed effective in both the design procedure and implementation. It is subject to certain recommendations for improvement and extension.

Space debris has become a rising problem in the aerospace community, leading to the need for effective spacecraft collision avoidance processes. Currently, these processes can be called unilateral as only one object in conjunction is considered maneuverable. This thesis focuses on the implementation of a combined action approach to collision avoidance and proposes a cooperation process between operators. The research objective is to optimize the dual maneuver solutions and explore negotiation proposals within a Space Traffic Management system.

The study utilizes an optimization algorithm based on multiple objectives, including propellant mass consumption, collision probability, and mission disturbance. The decision variables used in the optimization are related to the three-direction maneuvering within both objects in conjunction. The optimization is first carried out to minimize the three objectives listed above. These optimization results are considered preliminary as they do not allow for a proper trade-off for the operators. Hence, a review of the objectives used in the optimization algorithm yields the two new criteria used for the final results: the Collision parameter and the Cost parameter. The latter combines propellant mass consumption and mission disturbance.

The results, displayed as Pareto fronts, demonstrate that these objectives allow for the identification of optimal maneuver solutions. Adding on, a sensitivity analysis highlights the importance of precise maneuver timing and lower ΔV contributions within the solutions. The operator is recommended to re-analyze the CAM if the maneuver timing varies by more than 5 minutes. Through the exploration of various study cases and scenarios, insights are provided into the interaction between different systems in space. In general, the chaser showed higher values of ΔV magnitude than the target but the optimization results showed that both interacted together to reach the collision avoidance solution. The I_sp factor proved to not affect the optimization results significantly, and the single maneuvering spacecraft scenarios were successfully solved with the optimization method. This scenario led to higher Cost parameters and higher Collision parameter, the P_c could only be lowered slightly further than 10^-10. As this is below the defined threshold, the results were accepted.

In addition, a proposal is drafted for a communication flow and cooperation framework. The Middle Man, acting as a central authority between the two parties, facilitates the cooperation process, ensuring fair and efficient collaboration between operators. The proposed framework for decision-making is called "rule and resource following shared approach". While specific rules and procedures are not defined in this thesis, the framework allows for them to be included once agreed upon by operators. This thesis concludes that the proposed combined action and cooperation process offers potential solutions to the challenges posed by space debris and contributes to the safety and sustainability of space activities.

While the space industry expands rapidly and space exploration becomes ever more relevant, this thesis aims to automate the design of Multiple Gravity-Assist (MGA) transfers using low-thrust propulsion. In particular, during the preliminary design phase of space missions, the combinatorial complexity of MGA sequencing is large and current optimisation approaches require extensive experience and can take days to simulate. Therefore, a novel optimisation approach is developed -- called the Recursive Target Body Approach -- that uses the hodographic-shaping low-thrust trajectory representation together with a combination of tree-search methods to automate the optimisation of MGA sequences. The approach gradually constructs the expected optimal MGA sequence by recursively evaluating the optimality of subsequent gravity-assist targets. Moreover, the approach includes novel figures of merit as well as parallelisation concepts to increase the robustness and accelerate the convergence. An Earth-Jupiter transfer with a maximum of three gravity assists is considered as a reference problem. Extensive tuning improved the quality of the MGA trajectory representations substantially and as a result, a robust low-thrust trajectory optimisation could be ensured. A distinct group of highly fit MGA sequences is consistently found that can be passed to a higher-fidelity method. In conclusion, the Recursive Target Body Approach can automatically and reliably be used for the preliminary optimisation of low-thrust MGA trajectories.

The accurate prediction of satellite orbits is essential for the proper functioning of space-based services such as navigation, communication, and Earth observation. However, atmospheric drag is a significant source of error in satellite orbit prediction, especially in Low Earth Orbit (LEO), where the majority of space objects operate. The thermosphere, the outermost layer of Earth's atmosphere, plays a crucial role in determining atmospheric drag. However, the thermospheric density is subject to high levels of uncertainty, up to 30%, when computed from atmospheric models. This uncertainty is particularly relevant in LEO, where it can affect the operational lifespan of satellites.

To increase the accuracy of thermospheric density models, this paper presents an assessment of the accuracy of orbit predictions using empirical Thermospheric Mass Density (TMD) observations obtained from the Swarm C satellite. The study uses a Principal Component Analysis (PCA) to decompose a fine grid of density into the main temporal and spatial modes. Then, each of the modes is calibrated with Swarm C observations using a Least Squares Estimation (LSE) algorithm. The calibration is validated with the observation using unbiased metrics. These observations were assimilated into the NRLMSISE-00 atmospheric model, and the resulting calibrated density model was used to predict the orbits of Swarm C, GRACE-FO and Sentinel 1-A satellites. To analyse the consistency of the results, a slicing window analysis was performed, and the median evolution of the windows was computed.

The results of the study indicate that a calibrated density model with a several satellite geometries, such as the cannonball model, a panel model, or a scaled panel model, can significantly improve the accuracy of orbit predictions in LEO. During March 2022, a period of medium solar activity, the median accuracy of orbit predictions for Swarm C was reduced from 20.67 km with NRLMSISE-00 to 13.75 km with the calibrated model and a scaled panel model geometry. During the same period, the median accuracy of orbit predictions for GRACE-FO C was reduced from 17.98 km with NRLMSISE-00 to 2.89 km with the calibrated model and an (unscaled) panel model geometry. These findings have important implications for the sustainable operation of satellites in the increasingly crowded space environment.

Now that a rocky planet is confirmed to orbit in the habitable zone of our closest stellar neighbor Proxima Centauri, the interest in visiting that system is growing; especially since Breakthrough Starshot proposed a fly-through mission of the Alpha Centauri system by sending a swarm of laser-driven photon sails. While many engineering problems still need to be solved for such a mission to succeed, research has shown that futuristic, theoretical photon-sail configurations can reach the Alpha Centauri system within 75-80 years while also getting captured in a bound orbit about one of the binary stars. This paper investigates trajectories from the binary star system towards planet Proxima b. A mission to Proxima b is scientifically grounded since measurements or pictures could help us better comprehend the evolution of rocky planets and potential life-formation in our Universe. The classical Lagrange points in the binary system (AC-A/AC-B) and the system Proxima Centauri-Proxima b (AC-C/Proxima b) are used to find possible trajectories towards Proxima b. The transfer is divided into a departure phase from AC-A/AC-B and an arrival phase to AC-C/Proxima b. Heteroclinic connections are then exploited using a patched restricted three-body problem method to connect the two phases. A grid search is applied on the optimization parameters to explore the design space, after which a genetic algorithm is applied to further optimize the link, focusing on minimization of the position, velocity, and time error at linkage. Futuristic sail configurations are used, including double-sided reflective sails and lightness numbers up to ß = 1779. The design space exploration shows that a double-sided sail provides little improvement over a one-sided sail, mainly due to the constant sail attitude along the trajectories. Results from the genetic algorithm show that a transfer from the L2-point in the AC-A/AC-B system to the L1-point in the AC-C/Proxima b can be accomplished with a transfer time of 235 years for the one-sided graphene-based sail with a surface of 315x315 m^2 carrying a payload of 10 grams. A transfer from the L2-point in the AC-A/AC-B system to the L3-point in the AC-C/Proxima b, with a smaller one-sided graphene-based sail (75x75 m^2, carrying a payload of 10 grams), results in a transfer time of 1025 years. For both sail configurations, the position error at linkage is kept below 1% of the total travel distance, the velocity error below 1% of the velocity at linkage, and the time error below 1% of the total transfer time.

This thesis proposes an unsupervised Physics-Informed Neural Network (PINN) for solving optimal control problems with the direct method to design and optimize transfer trajectories. The network adheres analytically to boundary conditions and includes the objective fitness as regularization in its loss function. A test scenario of a planar Earth-Mars low-thrust optimal-fuel transfer and rendezvous is chosen. Comprehensive examination of training strategies reveals that convergence is highly dependent on the initialization of the network and that correctly balancing loss terms is essential for navigating the intricate loss landscape. This balance is achieved by carefully selecting loss weights and implementing a refined learning rate schedule. Comparative analysis to hodographic shaping solutions demonstrates that the PINN effectively identifies near-optimal solutions across a wide range of initial and final constraints for the Earth-Mars transfer problem, with a maximum improvement of 4.5 km s⁻¹ and median improvement of 0.55 km s⁻¹. The PCNN shows promise as a preliminary design tool for trajectory optimization in nonlinear dynamics.

Reinforcement Learning is being increasingly applied to flight control tasks, with the objective of developing truly autonomous flying vehicles able to traverse highly variable environments and adapt to unknown situations or possible failures. However, the development of these increasingly complex models and algorithms further reduces our understanding of their inner workings. This can affect the safety and reliability of the algorithms, as it is difficult or even impossible to determine which are their failure characteristics and how they will react in situations never tested before. It is possible to remedy this lack of understanding through the development of eXplainable Artificial Intelligence and eXplainable Reinforcement Learning methods like SHapley Additive Explanations. In this thesis, this tool is used to analyze the strategy learnt by an Actor-Critic Incremental Dual Heuristic Programming controller architecture when presented with a series of pitch rate and roll rate tracking tasks in a variety of non-linear flying conditions, which include flying close to the stall regime, experiencing non-linear reference signals, reaching the control surface deflection limits, and flying with large sideslip angles. This same controller architecture has been previously explored with the same analysis tool but limited to the nominal linear flight regime, and it was observed that the controller learnt linear control laws, even though its Artificial Neural Networks should be able to approximate any function. This thesis shows that, even in the non-linear flight regime, it is still more optimal for this controller architecture to learn quasi-linear control laws, although it seems to continuously modify the linear slopes as if it was an extreme case of the gain scheduling technique. Additionally, a more complex Reinforcement Learning architecture based on the Soft Actor-Critic algorithm is also explored with the same analysis tool, to demonstrate its usability in the presence of non-linear control laws and to improve our understanding of this offline state-of-the-art algorithm.

The number of objects in space is increasing over time, and therefore it is desired to find more efficient propagation models in terms of accuracy and computational speed. This thesis project focuses on the propagation of the state and uncertainties of Potential Hazardous Asteroids, to predict potential Earth impacts. A machine learning technique is proposed to reduce the computational expensiveness of current propagation techniques. The PHA position and the error made by the uncertainties are predicted using a neural network, which uses data from currently known PHAs. Several options are considered regarding the input and output variables and the networks are also tuned. The aim for the best model is to find a reasonable accuracy for the prediction of the position and the uncertainty of newly discovered PHAs.

Quasi-satellite orbits (QSOs) are orbits around the smaller primary in three-body systems. The MMX mission (developed by JAXA, to be launched in 2024) will use QSOs to orbit Phobos, before landing on its surface. This work studies the feasibility of using the invariant manifolds of three-dimensional QSOs for designing landing trajectories, using the Mars-Phobos system as a case study.

Applying continuation and bifurcation-analysis techniques, different families of QSOs were computed and their invariant manifolds propagated. Using a set of favorable manifolds as a reference, multiple maneuvers were designed via multiple-shooting and optimization techniques, producing feasible landing trajectories. The robustness of these trajectories was analyzed using a stability index and Monte Carlo simulations.

It was found that using the invariant manifolds of QSOs to land is only possible for some families of QSOs. However, for these, the manifolds allow generating robust and propellant-efficient landing trajectories that are able to reach most of Phobos' surface.

In the future, Air Traffic Controllers are expected to work together with more advanced computer-based automation that can automatically take action. The main challenge is then how to design computer-based tools such that they foster acceptance among air traffic controllers. One possible approach to foster acceptance is by matching the automated decisions and actions to individual human problem-solving styles, the so-called strategic conformance. Another approach is by making the automated tool more transparent and thus interpretable. Previous research aimed to combine these two approaches by making use of the Solution Space Diagram, a decision-support tool for Conflict Detection and Resolution, as a visual feature for a supervised machine learning method that aimed to generate individual human prediction models. Results were promising, but prediction accuracy could be significantly improved. In this study, the impact of feature engineering and a revised machine learning architecture on prediction accuracy will be investigated. This is done by evaluating different feature engineering and architecture options using data generated by a simulation in which Conflict Detection and Resolution is performed. It was found that a Convolutional Neural Network can accurately predict exact resolutions using regression and a more optimized architecture is introduced which significantly increases predictive performance. Furthermore, it is concluded that a larger solution space results in a slight increase in predictive performance whereas the use of a color scheme with more colors does not necessarily result in a higher predictive performance.

Optimising spacecraft reorientation manoeuvres with respect to time, energy and propellant is essential for achieving a high mission performance. Solving this reorientation problem in real time and onboard is considered to be a promising opportunity to achieve further efficiency improvements in space missions. However, the nonlinear programming (NLP) methods that are currently used are too slow for onboard implementation. Therefore, in this study, a sequential convex programming (SCP)-based optimisation algorithm was developed to solve the spacecraft reorientation problem in real time. Several features are implemented and proposed to improve the algorithm performance compared to existing studies, amongst others: an alternative transcription technique, a method to reduce inter-nodal constraint violations and an improved initialisation strategy. Furthermore, an extensive Monte Carlo campaign was conducted to thoroughly assess the performance of the SCP algorithm. While showing similar performance in terms of robustness and optimality, the median computational efficiency of the SCP algorithm was found to be up to a factor 6.1 higher than a state-of-the-art NLP benchmark algorithm. All results considered, it can be concluded that SCP-based optimisation algorithms are a promising candidate for solving the spacecraft reorientation problem onboard.