Landing a rocket on Earth is a key factor in enabling quicker and more cost-effective access to space. However, it poses significant challenges due to the highly uncertain environment. A robust, reliable, and real-time capable Guidance, Navigation, and Control (GNC) system is essential to guide the vehicle to the landing site while meeting terminal constraints and minimizing fuel consumption.. A 6-Degrees Of Freedom (DOF) flight simulator is developed, including accurate vehicle and environmental models such as variable Mass, Center of Mass and Inertia (MCI), winds, and detailed aerodynamics. Furthermore, not only thrust magnitude and engine deflections are used as controls, but also two sets of aerodynamic fins, as they are present in real rockets. Finally, initial condition dispersions and uncertainties in dynamics, navigation, and controls are included, to assess the robustness of the GNC strategy.
This study applies Meta Reinforcement Learning (meta-RL) to the terminal rocket landing problem. Through repeated interactions between an agent and its environment over multiple episodes, a Neural Network (NN) develops a policy that maps observed states to control actions. Unlike traditional methods, meta-RL leverages both current and past observations to output the optimal action, enhancing the robustness of the policy. Recurrent Neural Networks (RNNs) like Long-Short Term Memory (LSTM), are commonly used in meta-RL for their ability to handle sequential data. However, this thesis investigates also the use of attention-based Gated Transformer XL (GTrXL) networks, which are promising to improve the solution accuracy.
This research confirms this hypothesis, demonstrating that the GTrXL-based policy significantly outperforms the LSTM-based one. This policy is also less sensitive to internal hyperparameters, provided that the NN has a sufficient number of weights and biases to capture all the relevant features. The results indicate that incorporating complex vehicle and environmental models, along with dispersed initial conditions, aerodynamic and wind uncertainties, navigation and control errors, and actuator deflection rate constraints into the training process, yields a robust GTrXL-based policy. This guidance policy is able to meet terminal constraints on position, horizontal velocity, vertical angle, and angular rates in 1000/1000 simulations. The only exception is the vertical velocity which is exceeded on average by only 2-3 m/s. Including a thrust rate constraint mitigates this issue, reducing the average violation to 1 m/s. Finally, developing a terminal patch to increase the thrust magnitude in the last few meters before touchdown completely eliminates the vertical velocity issue, producing a 100% success rate, with all the Monte Carlo runs meeting all the terminal constraints. Moreover, the NN produces a solution in about 6 ms, showing great potential for its real-time use. The results are consistently superior to those of a conventional Guidance and Control (G&C) strategy where a Linear Quadratic Regulator (LQR) controller tracks an optimal trajectory, consuming only 6% more fuel.
Recommendations for future works include improving the reward function to better handle the vertical velocity constraint, and penalizing control effort to have smoother Thrust Vector Control (TVC) and fins control profiles. It is also suggested to expand the simulation scenario, including the unpowered aerodynamic descent.

As the Moon reemerges as a renewed fronteer in space exploration, the Lunar Zebro project proposes to deploy a swarm of miniature rovers for efficient lunar surface exploration. One of their goals is to leverage recent advancements in deep learning and AI-accelerating hardware, in conjunction with Commercial Off-The-Shelf technologies and the NewSpace movement, to enhance the autonomous capabilities of these nano-rovers. This research focuses on integrating AI-accelerating hardware within the stringent Size, Weight, and Power (SWaP) constraints of these lunar rovers. It evaluates the suitability of various hardware configurations. A Convolutional Neural Network for hazard detection was trained and tested on different devices and scenarios. Finally, the operational cycle of the rover was simulated and the constrained resources were tracked for the different design options.

The Mars Ascent Vehicle will bring samples back from the surface of Mars to its low orbit in 2031. Its preliminary design has been performed by NASA, defining it as a two-stage solid propellant rocket. However, a research gap exists in finding what the optimum solid rocket motors geometries are to bring the Martian samples to a target orbit between 300km and 375km above Mars.
To this extent, the ascent and propellant burn have been modelled, allowing for variation in the geometry of the motors, but also in the launch and separation angles, and in thrust vectoring control. This model has been incorporated into an optimisation, from which a minimum lift-off mass of 343kg was found, 57kg less than required. In the process, 18 thousand different solutions have been found that satisfy all requirements, would the priority shift from the mass of the vehicle to for instance the burn time, or the final altitude.

The growing satellite congestion in Earth orbits increases the risk of Kessler Syndrome, that could potentially hinder humanity's activities in space. This problem could be tackled with Active Debris Removal or On-Orbit Servicing missions. This thesis project aimed to investigate the combined controller approach for OOS mission reach phase, in which both the s/c base and robotic manipulator are actively controlled by a single control system. The multi-body dynamics were defined with the SpaceDyn toolbox in Matlab and the advanced control method was chosen, Model Predictive Control (MPC). The advantage of MPC is the optimization-based strategy, a straightforward definition of constraints and dynamics prediction within the future horizon. The NMPC approach with successive linearization was designed and verified, the controller was tuned and the control system was tested in simulated scenario cases. This study was performed in collaboration with the German Aerospace Center DLR within RICADOS project.

The plasma behaviour in an air-propelled Hall thruster (SITAEL HT5k) is investigated by means of numerical modelling and experimental measurements. The air plasma employed (nitrogen/oxygen mixture) is firstly chemically characterized, identifying the most relevant species produced: four neutrals (N₂, N, O₂, O), four positive ions (N₂⁺, N⁺, O₂⁺, O⁺) and electrons. Then, it is explored the kinetic theory of ion-ion streaming instabilities in the presheath, relevant to electropositive multispecies plasmas, extending it to the four-ions case under study. These results are used to adapt a non-stationary 1D fully fluid Hall thruster model to operation with an air plasma. Calibration with an experimental discharge current signal confirms the physical validity of the formulation in reproducing breathing mode oscillations, even in multispecies plasmas. The experimental data also consist of fast-diving triple Langmuir probe measurements, which are used to validate the model, showing reasonable accuracy of the spatio-temporal distributions of the plasma properties.

Satellite tracking is used to predict a satellite’s orbit to communicate and perform other key functions. It is commonly performed using large ground-based radars with an accuracy of 1 to 10 km or specialized onboard satellite systems. Ground optical systems are a proliferating technology for satellite tracking, identification, and communications. Such optical systems have a narrow field of view, and to ensure smooth and quick satellite acquisition, the satellite must be tracked with higher accuracy (in the order of 100 m). Hence, to allow for optical ground systems to function smoothly they require satellite tracking systems providing sufficient accuracy which is not common for small satellites.
This work analyses two radio-based methods to provide sufficiently accurate satellite tracking for the next TU Delft satellite to be acquired optically. These methods utilize the innate satellite radio communications subsystem of the satellite without modifications. To optimize the analysis work, the model complexity was gradually built up. The first method analysed is phase interferometry, which based on a first-order model, turned out to be non-viable. The second method investigated is time difference of arrival. Increasingly more sophisticated models were developed to understand its performance and limitations. For example, for Delfi-PQ, by utilizing 25 ground stations in a square configuration with 1000 km baseline and a noise level of 236 ns (71 m), the target satellite in a 500 km circular orbit can be acquired, with at least the required accuracy, for 18% of the area where the satellite can be received by a minimum of 4 ground stations. It is expected that this performance can be improved through future work. Specifically, by utilizing multiple measurements taken at different times, the satellite orbital parameters can be estimated directly.
We conclude time difference of arrival to be a promising method for further research, development, and eventual implementation by TU Delft. The approach and structure of an even more sophisticated model is detailed for future work.

Programmers usually write test cases to test onboard software. However, this procedure is time-consuming and needs sufficient prior knowledge. As a result, small satellite developers may not be able to test the software thoroughly.

A promising direction to solve this problem is reinforcement learning (RL) based testing. It searches testing commands to maximise the return, which represents the testing goal. Testers need not specify prior knowledge besides the reward function and hyperparameters. Reinforcement learning has matured in software testing scenarios, such as GUI testing. However, migration from such scenarios to onboard software testing is still challenging because of different environments.

This work is the first research to apply reinforcement learning in real onboard software testing and one of few studies that perform RL-based testing on embedded software without a GUI. In this work, the RL agent observes current code coverage and the interaction history, selects a pre-defined command, or organises a command from pre-defined parameters to maximise cumulative reward. The reward function can be code coverage (coverage testing) or estimated CPU load (stress testing). Three RL algorithms, including the tabular Q-Learning, Double Duelling Deep Q Network (D3QN), and Proximal Policy Optimization (PPO), are compared with a random testing baseline and a genetic algorithm baseline in the experiments.

This study also performs regression testing with a trained RL agent, i.e., to test a version of onboard software that it has never seen before. To do that, the agent processes graph input with code coverage information. The graph is extracted from the onboard software source code via static code analysis. The work tries two graph neural network architectures (GGNN and GAT) with several graph pooling mechanisms to process the graph input.

Apart from the test command generation algorithms, some middleware is also implemented, including a command/response parser, a state identification module, a branch coverage collection tool, and a tool to extract the graph representation and node features. During onboard software testing, the onboard computer (OBC) or the electrical group support equipment (EGSE) can be the master of the bus. The command generation algorithms can run on a lab PC or a cloud server.

The research reveals the advantages and drawbacks of using reinforcement learning to test onboard software. On the one hand, RL-based testing performs well in non-deterministic environments (e.g., stress testing) and regression testing. On the other hand, more straightforward methods like random testing and the genetic algorithm are more useful in deterministic environments.

This document also introduces relative background knowledge. It leaves many recommendations for future work, such as improving sampling efficiency, generalization, and learning a model for fault detection in satellite operation.

Stereo matching, the process of inferring depth maps from stereo images, is one of the most heavily investigated topics in computer vision. It is part of the first module of navigation systems of planetary rovers, e.g., NASA’s Mars Exploration Rover (MER) missions, NASA’s Mars Science Laboratory (MSL) mission, and ESA’s ExoMars mission. Many stereo matching algorithms, traditional and deep learning based, are available and their performance is typically evaluated on indoor objects or outdoor city scenes. In this thesis, our goal is to improve insight into the performance of stereo matching algorithms for the Martian surface on a single-board computer. First, we search for and compare stereo matching algorithms ranked on stereo vision datasets to obtain a manageable set of algorithms and verify their implementations. Second, we derive performance metrics from the requirements and validate our set of verified algorithms on the Katwijk Beach Planetary Rover dataset. Through experimental evaluation, we gain insight into the performance of four stereo matching algorithms.

This thesis was aimed for the Master of Science degree in Mechanical Engineering at The Delft University of Technology. The goal of this research was to formulate an assignment algorithm for autonomous guidance, control, and navigation system for spacecraft formation flying using a Model Predictive Control approach for estimation of fuel expenditure. This report is split into three parts. First, the earth's gravitation field and its variation are discussed. The relative motion in the orbit around the earth is briefly explained. The derivation of linearized relative dynamics by Clohessy-Wiltshire is presented. In the second part, the assignment problem is explained. The majority of the literature has been using the distance between the deployed state to the target state. A new approach is discussed using fuel as the assignment parameter including the framework for the assignment parameters is established. For the third part, fuel expenditure estimation using two types of control schemes is explained. The Artificial Potential Function approach is concisely explained. The choice of Model Predictive Control over Artificial Potential Function is discussed. The aim for the third part was the formulation of the control problem using the Model Predictive Control with a linearization dynamics model of the satellites. The system model used in the MPC formulation was constructed based on the Clohessy–Wiltshire relative equations of motion. The demonstrations were established using randomized positions. The simulations were performed and estimation of the fuel expenditure was explained based on the simulation results. The proposed assignment algorithm was tested using the fuel cost estimations from the MPC-based simulations. The results showed that the algorithm performed as designed. Lastly, the possible future research options are listed out.

As a part of a Future Launcher Preparatory Programme contract issued by the European Space Agency, the aerospace startup company Dawn Aerospace is developing an integral, additive manufactured (AM), liquid rocket engine. This 2.5 kN class engine is propelled by the storable propellants 90% hydrogen peroxide and kerosene and used in the application of a (sub)orbital spaceplane. In this study, a preliminary design for the thrust chamber of the AM engine is proposed, which is realized from the superalloy Inconel 718. To avoid the chamber wall from melting under the extreme thermal loads which originate from the ongoing combustion reaction, both regenerative cooling and film cooling with hydrogen peroxide are considered. The proposed designs are based on a custom-developed 2-D numerical analysis code, which considers non-linear steady-state heat transfer and linear elastic structural deformation. The results of this code are validated against hot-fire tests of both regenerative cooled and film cooled thrust chambers, propelled by 90% hydrogen peroxide and kerosene. Moreover, an immersion screening test campaign is conducted to validate the chemical compatibility of AM Inconel 718 with 90% hydrogen peroxide. This thesis compares the proposed AM chamber designs to the current bimetallic chamber design that is already developed and hot-fired by Dawn Aerospace. The bimetallic design comprises a coated copper-alloy liner and relies mostly on non-AM production techniques. The coating is introduced to improve the chemical compatibility with hydrogen peroxide. Simulations show that the total available delta-v of the spaceplane is reduced from 3.46 km/s to 3.38 km/s when the bimetallic design is replaced by an AM Inconel 718 design, relying on regenerative cooling and film cooling. On the contrary, the AM design may offer a thrust chamber dry mass reduction of more than 75%, whereas the total number of sealing interfaces can be reduced by more than 50%. In addition, the inspection-heavy coating, which is plated onto the copper alloy liner of the current bimetallic engine design, can be removed in the AM design.

Various machine learning algorithms have been applied to find optimal lowthrust
satellite trajectories, however, no fair comparison of their accuracy has been made yet. In this paper, two common and promising supervised machine learning algorithms are compared for their regression capacities:
the artificial neural network and the Gaussian process. A grid search is performed to evaluate the performance of the algorithms on different datasets, varying the input features for training, and the hyperparameters of the algorithms. The best performing Gaussian process and artificial neural network are applied as surrogate in a model that optimizes a trajectory from Earth to Mars using a differential evolution optimization strategy. The performance of the models is evaluated on both the Euclidean distance between the inputs corresponding to the predicted minima of the surrogate method and the nearest local minimum obtained with the shaping method, and the accuracy of the predicted Δ푉 budget required. For both quantities, the Gaussian process outperforms the artificial neural network. The minimum required Δ푉 budget to reach Mars was predicted more accurately, and the duration of the trajectory and the best moment to launch were found more often and accurately as well.

Humans are embarking on a new era of space exploration with the plan of sending crewed spacecraft beyond Low Earth Orbit (LEO), to the Moon, Mars and beyond. NASA is committed to land astronauts on the lunar surface by the year 2024. The goal of NASA's lunar exploration program - Artemis, a collaboration with commercial and international partners including the European Space Agency (ESA), is to establish sustainable exploration by the end of this decade. The plan is to use what is learned on and around the Moon to take the next giant leap, namely sending astronauts to Mars. Activities planned during the Artemis missions, especially in the early phases, involve finding critical resources needed for long-term exploration, and acquiring more knowledge on Moon, Earth and the universe by carrying out experiments. All these activities will involve extensive lunar geological field work, which is orders of magnitude more complex than field geology on Earth. Extravehicular activities (EVAs) will become increasingly more complicated than the tasks executed during the early Artemis missions and generally during human spaceflight missions so far. EVA systems and crewmember skills that currently do not exist will be required. This plan entails many challenges as real-time support from ground control cannot be provided to astronauts who thus need to become more autonomous. Hence, modern human-machine interfaces have to be designed to support astronauts during their deep space missions. Augmented reality (AR) and the internet-of-things (IoT) are changing the way industries work, especially AR has found application for space applications, specifically for procedural work. Nevertheless, only one AR space-related study focused on the use of AR for future human planetary exploration, namely on navigation and traverse planning. While cuff-checklists guided Apollo astronauts on the Moon, wrist displays and tablets represent the standard tools during today's astronaut analog planetary EVA missions. However, these are often operationally unfeasible as crew has to handle several tools simultaneously and/or repeatedly look at the display and thus gets distracted from the surroundings leading to a potential loss of situational awareness, affecting their safety. Based on the research that is currently being performed on IoT technologies in combination with AR for visualisation and enhanced situational awareness purposes, the benefits obtained through the use of these technologies applied to future human planetary EVAs, more specifically geological site inspections, were explored in this research. The AR-IoT surface exploration tool developed for this research introduces a new approach for astronauts to carry out geological site inspections. The tool enables hands-free operations such as data logging, detailed photo-documentation, taking site coordinates, descriptions of sites through the presence of a verbal “field notebook”, as well as mapping and highlighting features during a traverse by creating waypoints, while providing crew with suit diagnostics. A user-centered design method was adopted to design the AR-IoT tool deployed on the Microsoft HoloLens. This highly iterative design process involved two to three expert reviews for each of the first three concepts, and three heuristic evaluations for the fourth concept until the subsequent generation of the first prototype. Key usability and user interaction aspects, pertinent capabilities determining the adoption of innovative interfaces, essential insights into future human-machine interaction and design requirements for AR meant for EVA astronauts were gathered through semi-structured interviews held with four ESA astronauts and astronaut geological field activities experts. The interviews together with the qualitative and quantitative data collected through the questionnaires were then used to assess the usability of the AR-IoT tool. Moreover, these data provided with additional knowledge on user-centered design AR studies in general but also and particularly on user-centered AR space-related studies. Valuable insights into interface design and user interaction aspects were gained. Results from the qualitative content analysis of the interviews stressed the importance of user satisfaction (32% of 139 quotes) as a usability aspect. Key design factors identified were: displaying solely important information in the field-of-view while adjusting it to the user's visual acuity, easy usage, simplicity, helpfulness and extensibility. User interaction was the second most mentioned (24% of 139 quotes) aspect. While multimodal interaction was considered feasible, no conclusions could be drawn on the most suitable combination of inputs. Nonetheless, most experts defined voice the most intuitive input. Based on the positive feedback from ESA astronauts and other experts, the AR-IoT proof of concept proved to be a potentially usable tool for future geological site inspection activities. The AR-IoT tool is therefore a promising asset for analogue training missions, such as Caves & Pangaea, and in the future for lunar geological field work. While limitations in both research and design are outlined, a set of recommendations aimed at warranting future testing and development of a more advanced AR-IoT tool for astronaut geological field activities is provided.

Mars is expected to become a focal point of exploration (human and robotic) in the near future. Extensive operations and continued human presence on the planet would require a robust space infrastructure. Be it navigation satellite constellations or scientific missions in low Mars orbits (LMO) and Areosynchronous orbits (ASO), every satellite would have a definitive period of operation after which it becomes derelict. At the end-of-life (EOL) the satellite could either be left unattended or dealt with in a sustainable manner. The first option is what has led to the problem of space debris in terrestrial orbits. To protect our access to Mars, proactive sustainability needs to be practised already in the design stages of such missions. This project aimed at providing graveyard orbit solutions in circummartian space for future Mars debris. 200-year period stability was studied for orbits using the symplectic integration technique. Extensive validations were performed and propagation settings were tuned to suit a variety of configurations. A plethora of graveyard orbit solutions were found and presented for orbits in ASO and LMO regimes. For example, it was found that transferring an ASO satellite to 400 km below the nominal orbit altitude would ensure a stability margin of +/-25 km for at least 200 years. The protected zones were found to be safe from debris even for an uncertainty in initial eccentricity of 0.01 and a tumbling satellite. Multiple orbital geometry orientations (combinations of semi-major axis, inclination, right ascension of the ascending node), satellite geometries (various values of area-to-mass ratios) and uncertainties were studied to produce a comprehensive analysis of long-term stability of potential graveyard orbits around Mars, making them attractive for such purposes.

The first close-up image of a black hole has been taken recently by means of a network of high-resolution telescopes situated all around the globe, also known as Event Horizon Telescope (EHT). In that regard, this Master Thesis reviews the feasibility of establishing accurate inter-satellite baselines in a two-satellite constellation of satellites placed in co-planar Medium Earth Orbits (MEO) equipped with telescopes to, amongst other applications, allow performing Very Long Baseline Interferometry (VLBI) on celestial bodies such as the supermassive black hole in the centre of our galaxy.

Regarding the workflow of this study, first a thorough analysis of the visibility conditions from the GPS and Galileo constellations has been performed for the pair of MEO-placed satellites. The results have shown that the receiver antennas need to adapt a nadir orientation to maximize the number of visibility contacts from mentioned GNSS constellations. Second, an assessment on the impact of different error sources in the absolute orbit accuracy has been conducted for the pair of satellites. The sensitivity analysis comprises errors that derive from realistic uncertainties in the geopotential model, GNSS orbits and clocks, satellites’ solar radiation pressure model, centre of mass and antenna reference point. The results have shown that the error sources that most affect the accuracy of the absolute orbit solution are disturbance aligned with the velocity component in both centre of mass of the satellites and reference point of the receiver antennas.

Furthermore, to achieve the high standards of relative navigation, optical ISL observations with micro-meter precision level were employed. This precision level was determined by the limits of the employed software tool, as the state-of-the-art precision of these measurements is at the nano-metre level. Due to the 1D nature of the ISL observation, it has been found that they cannot be processed alone and require the GNSS code and phase observations to be processed along with them. In these processes, the system architecture of the inter-satellite ranging system has been assumed to be two-way to rule out the errors that derive from the non-synchronization of the satellites’ clocks. The relative POD results have shown that the shorter the distance between the satellites, the higher the precision of the relative orbit solution in radial and along-track components, ultimately being limited by the precision of the ISL observations. The fact that the satellites are placed in co-planar orbits makes the cross-track component not be visible and probably yield conditioning problems to the LSQ matrix. Therefore, the impact of angular separations ranging from 1 to 10 degrees on the relative POD accuracy is also investigated. The most promising results come from the case in which the relative distance between the MEO-placed satellites is low (1,000km) and an angular separation in the orbital planes of 10 degrees has been induced. In that case, the relative orbit solution is improved in all components (radial, along-track and cross-track) and placed in the few millimetre level.

The next decade will see an unprecedented growth in space activity and usher in a new era in space. Together commercial parties, the so-called NewSpace operators, have announced large constellations that will increase the number of satellites in Low Earth Orbit from almost 2,000 satellites to 20,000. Simultaneously, enhanced space object tracking capabilities are expected to increase the number of tracked objects from 23,000 to 100,000. These developments will lead to a strong increase in dangerous conjunction events and the required number of collision avoidance maneuvers as well as more complex conjunction geometries for satellites in Low Earth Orbit. This research proposes a new approach to collision avoidance maneuver planning that produces a set of maneuvers to reduce the collision probability to an acceptable level for complex conjunction geometries without requiring human interpretation beforehand. The approach combines an accurate numerical propagation procedure to take advantage of the small position uncertainties of the Conjunction Data Messages published by the Combined Space Operations Center with a multi-objective optimisation procedure to avoid having to assign relative importance to optimisation objectives beforehand. The approach is tested against four different conjunction scenarios ranging from two objects to involving four objects proving that it efficiently finds solutions for future complex conjunction geometries. It is shown that the new approach finds the global optima in the objective space spawned by collision probability. Applying an intrack maneuver is the most propellant-efficient method for reducing collision probability. In contrast to most contemporary research, this research finds that additional radial and crosstrack maneuver components can offer a significant decrease in collision probability. The quality of the product of many NewSpace operators depends on the service level that is offered: the maximum interval between two satellite visits. Intrack maneuvers significantly change the orbital period and negatively impact the service level. This research investigates extending the problem definition by introducing constellation performance loss as a third optimisation objective. Extending the objective space by an additional dimension increases the required computational effort, but provides an interesting new perspective on the true optimality of maneuvers. The approach uncovers the three-dimensional Pareto front at the expense of 7,875 function evaluations showing both a set of solutions that are propellant-efficient, which were mentioned earlier, as well as a set of solutions that inflict little constellation performance loss. The latter set of solutions has a large radial or crosstrack component and almost no intrack component. It affirms the finding that large radial and crosstrack maneuver components can offer promising solutions for collision avoidance maneuvers. As an alternative for reducing constellation performance loss, this work investigated the application of a second maneuver after the dangerous conjunction situation had passed to restore the original orbit. This concept can offer benefits for simple conjunction geometries, but is infeasible for complex geometries where a large amount of propellant is required to reduce the collision probability. It was also proven that errors in the actuators can cause significant deterioration in the resulting collision probability and constellation performance loss.

To obtain unambiguous ground truth of water-ice residing in permanently shaded regions of the Moon and to characterise the local regolith, ESA considers a mission involving an instrumented penetrator implanted there by high-speed impact. Released into lunar orbit, the European Lunar Penetrator (ELUPE) Descent Module will autonomously traverse a controlled trajectory to its designated target. The associated attitude control problem involves highly nonlinear large-angle slew manoeuvres and unstable minor-axis spin manoeuvres. To establish a benchmark, a controller based on classical control techniques was designed, verified and tested in a simulation. Legacy control algorithms were implemented and extended. A thruster management function was developed to translate the control commands into thruster actions. For the simulator, accurate models of the descent module and its environment were created.

A Monte Carlo simulation was run to determine the success rate of the ELUPE mission from a descent-and-landing perspective, and to assess the performance of the controller under off-nominal conditions. From the results, it was found that the success rate was 58.5% for a surface slope of 20°, and 74.2% for a slope of 10° or lower. Key factors affecting the success rate were identified to be the centre-of-mass offset and the solid rocket motor thrust misalignment angle. As further constraining these parameters would be unrealistic, it was recommended to modify the thrust curve of the solid rocket motor to improve the success rate.

Analysis of the attack angle and the nutation angle just prior to impact, revealed their success criteria were met in 98.7% and 99.8% of all cases, respectively. These successful results confirmed the attitude control problem could be satisfactorily solved by a 'classical' controller. However, despite its good global performance, the proposed controller was found to also exhibit some serious shortcomings. For this reason, it was recommended to explore the possibilities for a different controller.

To the best of the author's knowledge, this thesis represented the first known attempt at designing a comprehensive controller for a fully actuated, thruster-controlled penetrator mission targeted for an airless body. In addition, it was the first known study to provide insight into the feasibility and success rate of such a mission from a descent-and-landing perspective.

The Laser Interferometer Space Antenna (LISA) is a European mission for the detection of gravitational waves in space set to be launched in 2034. The mission will see the deployment of 3 spacecraft in heliocentric orbit keeping a triangular formation with side length of 2.5 million km. Laser beams are exchanged between the spacecraft by means of suitably mounted telescopes (2 per spacecraft), with the objective of synthesizing a very-large baseline interferometer. The interferometric measurements are taken between free-floating test-masses placed inside the spacecraft. Due to the nature of the scientific objectives, the mission requirements on spacecraft-spacecraft pointing precision are exceptionally strict. Moreover, the formation needs to operate in almost perfect free-fall, therefore the solar radiation pressure needs to be continuously compensated for by the on-board thrusters. Gravitational wave signals are measured in the frequency bandwidth of 20 μHz to 1 Hz, requiring the vibrations in that domain to also be eliminated both for the attitude and the displacement. The task is made possible by the gravitational reference system, a complex device that keeps the test-masses from touching the walls of the spacecraft by applying on the latter an external force through μNewton thrusters. This mode of operation is called Drag-Free and Attitude Control System (DFACS). In this thesis we attempt to study and design a DFACS for LISA using a technique called Quantitative Feedback Theory (QFT). The design process starts from the definition of the orbits, the goal orientation of the spacecraft, the sizing of the solar radiation pressure induced disturbances and the derivation of the dynamics of the 19 degrees of freedom to be controlled. Using QFT, the design process is carried out on the DFACS using separation of the dynamics. As a result, analytical equations for the calculation of the LISA commands are derived and the methods to design a control system compliant to the scientific requirements imposed on the sensitivity are shown.

The Rotation and Interior Structure Experiment (RISE) on the InSight lander and the LaRa experiment on the ExoMars platform will provide precise measurements (accuracies of a few mas) of the rotation of Mars, in terms of the Mars rotation and orientation parameters (MOP). These parameters include the rate of precession and the amplitudes of the nutations, of the length-of-day (LOD) variations, and of the polar motion components. The MOP are sensitive to the interior properties of Mars, particularly to the presence and size of a liquid core. However, uncertainties of current interior models in other physical parameters describing the internal structure of Mars, such as the temperature distribution, the composition, and the elasticity, limit the information on the interior obtainable from the MOP. Current estimates of the core radius have uncertainties of about 100 km, and suggest a fully liquid core. The improvement of the accuracy of the MOP solution from the landers coming from the inclusion of radio-tracking data from Trace Gas Orbiter (TGO) was studied in this project. Both numerical (through the orbit determination software GINS and Tudat) and analytical (through the ORB software developed at the Royal Observatory of Belgium) methods were employed to asses the sensitivity of the TGO orbit and radio-tracking data to the MOP of interest. In particular, an analytical method based on Kaula’s equations was developed to predict the effect of the MOP on a wide range of orbits. The satellites most affected by CW were found to be those in near-circular orbits at about 40∘ of inclination. The orbiters most sensitive to LOD variations were those in near-equatorial, highly-eccentric orbits. As for the improvements on the MOP estimation due to TGO data, a covariance analysis was performed with different assumptions on the dynamical and observation models, and on the estimated parameters. The improvement with respect to a lander-only solution is of up to 6 % for the CW components, up to 8 % in the LOD variations components, and up to 20% and 12% on the nutations amplification factor and the FCN frequency (the latter two judged excessively optimistic). However, no definite inference on the resulting core radius accuracy improvement could be made.

As the complexity of high-tech systems continuously increases, engineers look for possibilities to reduce time and cost of the development of these systems. Architecture-based design enables a front-loaded design process with knowledge reuse. By enabling the automatic synthesis of simulation models, different configurations of an architecture can be realized and simulated efficiently. Current practices are found in the automotive and aerospace industry where architecture-based design is used for the automatic synthesis of multi-physics simulation models. In this way, different architecture options and simulation model variations can be efficiently investigated early in the development process. Multi-body simulations are also frequently used in the conceptual design of complex mechatronic systems. However a suitable methodology to synthesize their simulation models is lacking.

In this thesis, a methodology is developed that makes three necessary improvements to an existing approach that can synthesize only multi-physics models. Firstly, the existing approach assumed that the order of synthesis of the architecture was irrelevant. However, the use of modular and independent simulation models breaks associative links. Therefore, an additional sorting algorithm needs to be introduced during the synthesis of the architecture. An initial successful attempt is made by adding an implementation of Kahn's algorithm. A second addition of the thesis is the definition of a formalized modelling process for modular multi-body simulation models. The formalized process guarantees that the interfaces between simulation models will always work. It also simplifies the modelling approach which leads to the third improvement: clarity of design intent. A procedure was developed and automated that takes away the burden of creating interfaces between simulation models. Consequently, the modeller can focus fully on creating robust models with clear design intent.

The developed methodology is verified with a case study on the conceptual design of a trailing-edge high-lift system. An architecture is defined and parameterized multi-body simulation models are created that realize the components of the architecture. Besides the components that actuate the flap, such as gearboxes, shafts and a motor, four different deployment mechanism types are modelled. The parameterized geometry of the simulation models adapts automatically in order to provide the correct flap trajectory and to form a consistent simulation model. A tool synthesizes automatically all architecture configurations. Interface forces and moments between bodies can be inspected directly and the required actuation torque is found. The sizing of the components is not performed.

The methodology is evaluated for knowledge reuse. For the case-study, this leads from 41\% up to 92\% of reuse of simulation models. However, before these results can be generalized, a trade-off needs to be made from case to case between the time that is saved by the automatic synthesis of simulation models and the time it takes to create the architecture and the compatible simulation models. It can be concluded that the developed methodology enables an architecture-based design approach for complex multi-body simulation models. Furthermore, the methodology is advantageous compared to a traditional design process where all system configurations have to be modelled and analyzed individually.