This thesis presents a hybrid control framework that combines Reinforcement Learning (RL) and Model Predictive Control (MPC) to achieve constraint-satisfying flutter suppression and load alleviation in flexible aircraft subject to turbulent gusts. During training, MPC computes safe input bounds using high-fidelity Linear Parameter Varying (LPV) models and long prediction horizons, exploiting known disturbances to accurately capture aeroelastic behavior. A Q-learning agent is trained to select control actions within these bounds, adapting to nonlinear dynamics and actuator delay. At deployment, the learned policy operates from a lightweight Q-table, with certified interpolation ensuring constraint satisfaction even for unseen states. By integrating the anticipatory capabilities of MPC with the adaptability of RL, the framework enables effective control under turbulence and structural uncertainty. While demonstrated on an aeroelastic aircraft, the approach can be generalized to other systems with similar dynamics, where constraint handling and adaptive control under uncertainty are critical.

To accomplish the dream of continuous presence on the Moon, humanity has to face many challenges. A major one is to provide continuous electrical power to all the necessary installations on the surface during the long lunar nights or in the dark polar craters. This study explores the design and optimization of one solution to this problem: space-based solar power (SBSP). The SBSP system consists of one or more solar power satellites, generating power and transmitting it remotely to a receiver on the surface.

Two system concepts are investigated: a constellation of satellites in lunar elliptical frozen orbits and a single satellite in the L1 Lagrange point. Both concepts are designed to deliver at least 100 kW of continuous power to the lunar surface through laser power transmission. The study combines sizing of the spacecraft, the ground element, propagation of the final trajectory and a detailed model of the transmission efficiency to find an optimal design for both concepts. The space elements make use of concentrated photovoltaics, an active fluid loop and a high-power laser, while the ground elements are build out of modular elements of either stretched lens arrays or heat pipe cooled laser power converters.

Selected subsystems of the lunar orbit constellation have been designed in more detail, resulting in a 7 satellite constellation of 2247 kg per satellite a 816 kg modular ground element. Both the Lagrange point and lunar orbit architectures outperform alternative power systems (nuclear & microwave-based SBSP) in terms of launch costs by almost an order of magnitude on average.

Green hydrogen is emerging as a viable alternative to kerosene for medium-range aviation, with airports playing a critical role in the distribution of liquid hydrogen (LH2). This study uses a simulation-based optimisation model to investigate hydrogen refuelling operations at Rotterdam The Hague Airport. Different analyses reveal that on-time performance and capacity are strongly influenced by hydrogen-flight penetration rate, refuelling-truck fleet size, and turnaround times. Operational efficiency depends on maintaining adequate fleet sizing and minimising LH2 refuelling durations, while factors like truck speed and trailer swap time have limited impact in compact airport layouts. Moderate integration by 2040 requires gradual investment, but high penetration by 2050 demands substantial infrastructure and vehicle expansion. As hydrogen usage grows, a second refuelling truck becomes necessary between 22% to 39% penetration, depending on the specific day and required refuelling time. Also, end-of-day and multiple close LH2 departures risk exceeding allowable departure time slots. Safety zone constraints and trailer capacity trade-offs further highlight the need for coordinated planning. These results underline that a successful transition to hydrogen aviation hinges on strategic fleet sizing, optimised processes, and phased infrastructure upgrades to ensure reliable and sustainable airport operations.

Nonlinear Dynamic Inversion (NDI) control techniques provide a conceptually simple and modular control framework, making it an attractive technique for designing flight control laws with shorter design cycles. However, its lack of inherent robustness guarantees shifts the burden of the design from the synthesis to the analysis part. Conversely, H-infinity Loop-Shaping provides controllers with robust stability guarantees. This work proposes a novel framework leveraging the H-infinity Loop-Shaping Design Procedure to optimize a structured linear variant of Incremental Nonlinear Dynamic Inversion (INDI) control, a Hybrid IDI controller. The Hybrid IDI controller consists of a blend between classical model-based DI and sensor-based IDI. The proposed methodology is validated through the design of a pitch-rate controller for NASA's X-29 experimental aircraft. Results demonstrate that the approach achieves robustness guarantees comparable to standard full-order H-infinity controllers while maintaining the simplicity and modular architecture of NDI-like structures, thereby combining the advantages of both techniques.

The goals of governments and agencies around the world is to minimize fuel emissions for the upcoming decades. The Flying V offers a new concept, which is an aircraft of flying wing configuration, offering higher aerodynamic efficiency than conventional tube and wing configuration. However, such an configuration offers challenges with its stabilty and control characteristics, with difficulties arising in control authority, like for instance, longitudinal trim. This study aims to find the optimal control surface layout for the FV-1000 aircraft.First, the certification requirements relevant to each control surface to be sized in this study (aileron, elevator and rudder) are gathered, primarily based on CS-25 regulations, and they are expressed mathematically. AVL is chosen as the tool to conduct aerodynamic analysis, for sizing purposes. Two coefficients are compared for their viability as objective function for optimizing a control surface,hinge moment coefficient and drag coefficient, hinge moment coefficient turns out to be the better option, it also provides control surface for a given span as small as possible. A consistent optimization framework is applied to both the aileron and elevator, wherein the spanwise domain is discretized and various combinations of span length and hinge line chord percentage are evaluated. The resulting optimal configurations are spatially adjacent, with the sizing of each control surface constrained by the span available due to presence of the other. During validation procedure, the comparisons made between VLM and wind-tunnel when extrapolated to comparison between VLM analysis and full scale flight case, it is observed that aileron is oversized, (for time to bank) while control elevators are undersized (to be precise, for pull-up maneuver). Certain solutions like high-lift devices or other methods of reducing AoA are suggested to make control surface like elevator comply with requirements. Rudders are the final surface to be sized which satisfy requirements of OEI trim at VLM analysis and even for full scale case, but is not able to satisfy the Steady sideslip requirement by significant margin for both cases. However, these results signify need of research on other options to assist these control surfaces, like drag rudders to assist with directional control authority, high lift devices to decrease AoA, assist surfaces like elevator in meeting the certification requirement, which are discussed.

This research investigated the application of electrical thrusters within the Attitude Determination and Control System (ADCS) of the European Space Agency’s LUMIO mission, a 12U CubeSat set to op- erate in a quasi-periodic halo orbit around the Earth-Moon L2 point. While CubeSats have become pivotal tools for lunar exploration, the use of electrical thrusters for ADCS, particularly in the lunar en- vironment, remains under-explored. This study aimed to evaluate the impact of replacing LUMIO’s current ADCS design by electrical thruster configurations on attitude control performance, robustness, and connectivity, bridging a critical gap in current literature.
A simulation framework was developed to assess spacecraft performance over a two-week period, ex- amining the LUMIO mission reaction wheel set-up, as well as four distinct thruster configurations. Key metrics included thrust output, angular velocity, and pointing accuracy, represented by the half-cone offset angle. In addition, power and energy requirements were investigated. Robustness tests eval- uated system adaptability to single thruster failures, solar array deployment, and high initial angular velocities. Physical validation was achieved by porting the control algorithm to an embedded STM32 Nucleo board, which was connected to a solenoid valve representing a dummy thruster. This setup allowed for real-time testing of execution accuracy, signal fidelity, and system integration.
The results demonstrated that the control algorithm consistently maintained high pointing accuracy, keeping the half-cone offset angle within the mission requirement of 0.18 [◦]. Reaction wheels and electrical thrusters effectively generated the required control torques, with negligible angular momen- tum build-up in the nominal simulation. Overdetermined thruster configurations showcased enhanced energy efficiency, reducing total energy consumption by approximately 29% compared to determinate setups. However, the study also highlighted challenges, such as the minimum impulse bit constraints of the Pocket Rocket thrusters, which limited precise low-thrust operations.
Robustness tests revealed that determinate configurations failed to accommodate single thruster fail- ures, while overdetermined systems adapted effectively, albeit with increased power demands. From the results, it is recommended for any space mission to be certain that the on-board computer is aware of any thruster fail occurring. The undeployed solar array configuration significantly reduced energy re- quirements, supporting its feasibility for early mission phases. The thruster configurations showed not to be able to realistically counteract de-tumbling manoeuvres, significantly degrading their feasibility in the actual LUMIO mission. Additionally, the embedded system validation confirmed accurate command signal generation and execution, bridging the gap between simulation and real-world implementation.
Although the electrical thruster configurations adhered to pointing accuracy requirements in nominal simulation conditions, they impose significantly higher power and energy demands and are unable to counteract high de-tumbling manoeuvrers compared to the current reaction wheel configuration in the LUMIO ADCS design. Addressing main engine parasitic torques and exploring alternative, higher- performance thrusters may offer a feasible solution for integrating electrical propulsion into lunar Cube- Sat ADCS. While the reaction wheel and non-electrical thruster combination remains the preferable option for now, this research demonstrates the potential for electrical thrusters in advancing CubeSat capabilities. Future studies should build upon this foundation by addressing current limitations, extend- ing simulation durations, and exploring innovative propulsion technologies to unlock their full potential in deep-space missions.

Satellites are becoming smaller and therefore their attitude sensors must reduce in size as well. However, in order to maintain 3-axis attitude determination, a combination of several sensor types is required. Moreover, the availability of these sensors is mostly dependent on the lighting conditions and slew rate of the spacecraft. In this thesis, a prototype low-budget 2-in-1 star tracker and Sun sensor was designed with similar performance but reduced technical budgets compared to a typical combination of COTS attitude sensors on CubeSats. The design can be used in both sunlight and eclipse, while stable pointing and while tumbling and it takes up less volume on a satellite. Future designs should focus on reducing solving times and power consumption by creating a dedicated microcontroller and a more optimized star tracker algorithm. Alternatively, a different CMOS chip with lower power consumption should be selected or the use of a satellite's central computing unit needs to be considered.

his thesis introduces a Model Predictive Fuzzy Control (MPFC) framework for mission-planning in multi-agent systems operating in dynamic, uncertain environments. MPFC integrates predictive decision-making from Model Predictive Control (MPC) with the adaptability of Fuzzy Logic Control (FLC) to handle complex, time-sensitive tasks. A generic mathematical formulation is developed and applied to a multi-agent Search and Rescue case study with fire and wind dynamics. Comparative performance analysis against MPC and Pre-Tuned FLC controllers, along with sensitivity analyses and design exploration, demonstrate that MPFC can outperform existing approaches and maintain robust performance under varying conditions. Although this technique is promising, several limitations and challenges are identified, suggesting avenues for future research to refine and enhance its applicability.

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.

One of the biggest trade-offs in aviation is between a vehicle’s ability to take-off and land vertically and its efficiency in cruising flight. The recent drive for electric vehicles creates space to consider new solutions to this trade-off. Tilt wing aircraft offer significant advantages in vertical take-off and landing (VTOL) operations due to their versatile design. One of the difficulties in designing configurations with little historical data is that they are difficult to predict. The challenge of formulating a flight dynamics model valid through the transition phase from vertical to horizontal flight is undertaken in this study. This research aims to investigate the tilt wing aircraft behaviour through the wing tilt transition, as well as the stability characteristics. A six degree of freedom model implemented on the Canadair CL-84 Dynavert compares favourably to flight test data, albeit with low-fidelity especially around hovering flight. The likely cause is the simple rotor to wing slipstream interference modelled. The stability characteristics reveal large spikes in positive stability of the longitudinal derivative Cmu and Cmw due to the behaviour of the wings and the slipstream effect on the horizontal tail, with normalized values up to 5.5 and 150 respectively. Some unstable flight configurations are found within the flight envelope at 41 degrees and 28 degrees due to wing stall.

Trajectory optimization has proven to be a powerful tool in solving a wide variety of optimal control problems in the aerospace field. However, in many cases, numerical complexities prevent the analysis of optimal trajectories for high-fidelity models, particularly due to the inherent difficulty of transcribing high-order dynamic systems. This research project proposes a methodology incorporating reduced-order modeling that retains the most critical dynamic characteristics from a full-order model while allowing the resulting simplification to be manageable for a trajectory optimization solver. The study applies this methodology to evaluate optimal landing trajectories for the UNIFIER19 C7A, a hybrid-electric aircraft equipped with a distributed electric propulsion system that was previously developed under the UNIFIER19 project. Results show that the reduced-order models generated for the aircraft can be used to generate flyable trajectories, verified by tracking the resulting landing approach paths using the base high-fidelity model. It is envisioned that this methodology will also be applicable to other aircraft models and mission phases.

Urban Air Mobility (UAM) has seen remarkable growth over the past decade, especially considering the delivery of goods. Notably, the delivery of medical supplies via Unmanned Aerial Vehicles (UAVs) has emerged as a promising application, which is driven by its societal benefits and has been demonstrated successfully in rural environments. However, expanding these operations to urban areas presents a unique set of challenges, such as high traffic density and the need for online path planning of UAVs. Effective path planning and coordination are of utmost importance to allow for safe and efficient delivery operations in urban environments. Currently, path planning of UAVs is relatively unexplored, and for drone delivery models the trajectories of UAVs are often oversimplified. This research aims to fill the research gap by developing and evaluating an online path planning and coordination mechanism in 3D for a cooperative fleet of autonomous UAVs delivering medical supplies. The Rotterdam Area is selected as a use case, but the model can be adapted to other cities. Utilising an agent-based model formulation, this paper formulates a novel approach for multi-agent pathfinding in 3D and real-world environments, where the travelled paths of UAVs in drone delivery models are computed by taking into account the urban environment and kinematics of UAVs. Windowed Cooperative Safe Interval Path Planning (WC-SIPP) is used as a path planning and coordination mechanism, where kinematic constraints of UAVs and safety separation distances between UAVs are incorporated. Experimental studies show the capability of the algorithm to plan UAVs in different multi-layer urban environments. We establish that including more than one vertical layer is beneficial for UAVs to avoid conflicts by allowing them to change altitudes during flight. Additionally, we show that an increase in the number of layers does not lead to an increase in the delivery time of packages. However, an increase in available layers does come with a decrease in computational performance. Besides, the proposed method is able to plan the trajectories of UAVs and ensure safety separation between UAVs within a dynamic environment, where up to 20 unexpected obstacles are introduced into the environment. Furthermore, we demonstrated that for our operation model, assigning prioritisation based on either mission type or package request time did not influence the delivery time of packages.

The concept of Distributed Electric Propulsion (DEP) aircraft holds great promise in achieving the goals outlined in Flightpath 2050. This involves utilizing electricity to power propellers and enhancing overall flight performance through interactions between the wings and propellers. This thesis investigates the stability and control aspects of DEP aircraft compared to conventional aircraft. A tool is proposed to estimate the minimum horizontal tail and aileron size for different configurations, considering propeller effects. Analyzing three configurations (turboprop excluding propeller effects, turboprop including propeller effects, and DEP including propeller effects), propellers are found to destabilize the aircraft but enhance take-off rotation and provide additional damping during roll. The DEP aircraft exhibits a 19% smaller normalized horizontal tail size than the turboprop (with propeller effects), attributed to lower propeller destabilizing moments and a shorter fuselage. Despite a lower roll requirement, the DEP aircraft needs the same normalized aileron size due to larger rolling moment of inertia. A sensitivity analysis suggests a T-Tail is optimal for DEP aircraft stability, and adjusting battery placement and reserve fuel improves overall performance.

Inaccuracies in robotic arms can significantly hinder their performance in tasks where precision is critical. This thesis focuses on the kinematic calibration and elasticity compensation of a 6 degrees of freedom robotic arm integrated into the Lunar Rover Mini, developed in collaboration with the Robotics and Mechatronics Institute of the German Aerospace Center (DLR), Wessling. The arm, constructed using 3D-printed components and driven by affordable RC servo motors, experiences notable inaccuracies in end-effector positioning due to joint flexibility and structural deformation, especially under load. This research aims to enhance the arm's accuracy through a model-based calibration approach that compensates for both elastic deformations and geometric misalignments, addressing the absence of feedback sensors. This cost-effective approach, which requires only 3D measurements of the end-effector's position, has resulted in an approximately 80% reduction in the robotic arm's average position error, proving advanced robotic technologies can be more accessible for educational and research applications.

Ball valves are a critical component in a plethora of liquid propellant propulsion systems. The Spanish company PLD Space is considering the usage of these valves in their new series of KER-LOX liquid propellant rocket engines, the TEPREL-C & TEPREL-C Vacuum engines. These will be operating on-board the Miura 5 launch vehicle. The purpose of this research is to re-design and analyze an unoptimized ball valve design, as to make it suitable for cryogenic service while minimizing its mass. The secondary objective of the research is to inquire into the aspects of ball valve operation. This includes investigating the required operating torque of a ball valve and the achievable flow factor as a function of a ball valve's opening angle. The results of this thesis are scripts which can estimate the torque and flow factor values, a methodology to mass-optimize a ball valve, and a re-designed ball valve suitable for cryogenic service.

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.

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.

Performing crucial activities for space exploration, e.g., debris removal and on-orbit servicing, systems for Rendezvous and Proximity Operations (RPO) are required to be autonomous and scalable. Within this context, learning-based relative navigation has gained significant traction due to the latest advancements in AI and the cost-effectiveness of monocular cameras.

This thesis introduces a pose estimation system composed of an Object Detection (OD) and a Keypoint Detection (KD) network for keypoint extraction, coupled with a pose solver, and trained purely on synthetic images. When applying realistic data augmentations, the system achieves a reduction in KD error by 80%, further improved by training on photorealistic images. After an architecture optimization step, the final system consistently meets the inference time requirements on the Myriad X edge processor. This proves the feasibility of developing RPO systems with artificial data, showcasing a scalable approach, while complying with the limitations of onboard hardware.

CubeSat missions have been deployed to cislunar space and beyond, paving the way for the next significant advancement: a dedicated CubeSat mission to explore a planet near Earth. To contribute to this goal, the German Aerospace Center (DLR) has developed radiation-hardened small satellite technologies, including communications, power and onboard computer subsystems. This study presents a stand-alone Mars exploration mission using the CubeSat standard that will demonstrate DLR’s in-house technologies. This mission will perform an independent transfer to the Red Planet, achieve orbital insertion, and conduct measurements on its lower atmosphere and gravity field. A system concept has been created by integrating the in-house technologies, investigating the necessary Commercial-Off-The-Shelf (COTS) components, and performing the mission analysis to assess the feasibility of the mission. The resulting 12U CubeSat has a 20.8 kg wet mass, 6.3 km/s low-thrust maneuvering capability and can generate up to 90 W of power at Mars. The proof-of-concept mission is planned for a 4-year duration.

RLV development can be considered as the modern step towards mission design due to financial and strategic decisions. In the past, reusability has been addressed however the level of maturity of the technology, both in terms of hardware and software was not yet reached. There are several aspects to developing a RLV, and these can be categorized into optimization of the LV, optimization of the trajectory, and cost analysis. TO be able to determine the feasibility of the mission, it is not just necessary to develop a suitable configuration, but to also determine the physical feasibility of the trajectory. Several methods exist of which convex optimization is selected. This class of algorithms have risen in popularity in the regime of powered descent guidance, and present a desirable trade-off between performance and computational cost. An already existing algorithm, DESCENDO, for a two-staged vehicle CALLISTO purposed for a mission to a geo-synchronous orbit, is taken as reference. The algorithm is rewritten in YALMIP allowing it to perform more efficiently by saving computation time through creation of multiple controllers based on a discretized burn schedule. A potential candidate for reusability in the future is selected, which is a VEGA variant, considered as a two-staged vehicle with set requirements on the mission and configuration. Through closed-loop simulations, the feasibility of RTLS for a particular mission of this VEGA variant can be studied. The disciplines involved in the study include the launch vehicle optimization, engine sizing, preliminary ascent & descent, and 3-DoF simulations. Previous research at TU Delft on RLV has included the work Rozenmeijer, Vandamme, Van Kesteren, Miranda, and Contant, graduate students of the TU Delft Aerospace Engineering faculty. The work relied on the usage of the TUDAT C++ software environment and based its feasibility or reusability of operations through a cost-analysis. A shift in direction is taken away from cost-analysis to examine at a greater detail the physical feasibility of the trajectory for a nominal candidate RLV. This is done by examining the influence of simulator to guidance algorithm dynamics and guidance algorithm parameters. Moreover, a nominal payload class between 100 and 500 kg is selected to determine the configuration of the vehicle ideal for this mission. To be able to determine feasibility of RTLS, three metrics are considered, which are the final landing velocity, final landing position, and maximumdynamic pressure. The study performs higher fidelity analysis only on the return phase, and as such the starting conditions for descent are determined through a preliminary design process by considering a drag-less ascent. This returns a starting altitude of around 26-30 km, with similar values for starting downrange position, and varying conditions of initial mass and velocity. For the preliminary descent, it is found that the metric of dynamic pressure does not reach more than around 60% of the limit imposed by the VEGA-C, which is similar for other VEGA variants. This coincides with research done with the CALLISTO vehicle. All in all, the best cases for these metrics and one included as an overall best case where candidates for the 400 kg payload class. The selection criteria for best cases of the preliminary descent involved the dynamic pressure, final velocity, and required propellant mass for descent. Moreover, the vehicle optimization results showed that this contained the most variation of vehicle characteristics, and as such it was deemed as a desirable class to work with for its flexibility in design. The best case burnt mass result was selected as the best case velocity required extensive propellant mass to burn for only a less than 7 m/s difference in result, which would not be indicative of what the convex algorithm could achieve due to dynamics involved in the preliminary experiment. This nominal candidate is then tested for various variations of controller tuning parameters combinations, burn schedules, and simulator/guidance frequencies. The results showed a clear desirable region for the final time of just between 300 and 310 seconds for return, favouring shorter burns. The solution envelope for the burn schedule showed gaps in zones of feasibility as well as optimality, suggesting some performance issues with the algorithm due to it failing to find solutions. Nevertheless this envelope is well defined and several feasible solutions existed. The influence of parameter tuning and simulator frequency was studied. It was determined that no set of guidance parameters could give an advantage over the other, but that some values did favour feasibility more. This is somewhat in contrast to the selection of frequencies, as despite the fact there was also a large difference for higher frequency ratios between the Q3 to Q4 and Q0 to Q2, there is a noticeably trend that higher ratios are favoured. Moreover, a local optimal ratio of frequency of 10-1 was also found, and being the same ratio used for the other experiments as well as the CALLISTO study, provides more evidence that this effect is intended. The uncertainties studied are for the initial state variations, errors in reading of position and velocities, and process time delays. It was noted that almost all the errors in the initial state variations shared similar distributions for ranges of values of the metrics. The overall majority returned feasible as well as the large minority of this returned optimal. Of little to no significance was the processing time delay, of which the overwhelming majority returned optimal results, and the rest where outliers whose process time factor where beyond the 3σ limit imposed in the creation of normal random variables. The largest errors arose from the real-time uncertainty in the velocities and position, modelled after pseudo-range errors. Although results showed a high density in the feasible and optimal regions for metrics of final time and position, there was also a high density past the feasible regions. It can be considered that the feasibility of RTLS operations for such a VEGA vehicle is restricted by such errors as expected, but nevertheless results are promising in what can be the main steps to lead to an error analysis study by the use of state estimation techniques and testing various modifications made to the SOCP problem to improve performance.