Healthcare systems increasingly rely on robotic technologies to address workforce shortages, yet asignificant disconnect remains between the technical logic of efficiency and the relational requirementsof care. While standard Value Sensitive Design (VSD) provides a method for value integration, it lacksspecific normative content. Care Centered VSD (CCVSD) resolves this by anchoring design in careethics, yet it remains a theoretical model that lacks the practical tools required for engineering implementation.This thesis posits that this failure stems not from a deficit of moral will (practitioner apathy),but from a deficit of moral skill: the lack of intermediate-level knowledge (operational artifacts thatbridge the divide between high-level theory and concrete implementation) to translate abstract valuesinto concrete technical implementations.Adopting a Research through Design (RtD) approach, this study operationalized the CCVSD frameworkthrough a case study within a commercial robotics company, focusing on the development of a socialnavigation module (interaction-aware motion planning that respects human proximity and social cues)for the PAL Tiago robot. The investigation revealed three structural barriers. First, semi-structured interviewswith 19 stakeholders (comprising robotics engineers and caregivers) identified a semantic gapwhere groups held divergent interpretations of care values; this was bridged by the Prospective ValueHierarchy, which translated care values into technical constraints. Second, the technical implementationof the navigation algorithm revealed a normative void, caused by the original framework structurallyskipping the technical development process. This phase was made explicit and operationalizedby integrating standard engineering workflows, providing the necessary structure to document technicaltrade-offs as ethical decisions rather than implicit defaults. Third, the integration phase of theframework, executed through comparative evaluations with 20 stakeholders (spanning care receivers,caregivers, and management) uncovered contextual instability, demonstrating that identical robotic behaviorselicited divergent value judgments across different care settings.The findings from this case study suggest that the operationalized framework offers practical utility forengineering teams. Feedback from the participating engineers indicated that the translational artifactshelped structure their tacit knowledge into a formalized process. These results indicate that providingengineers with tools to convert ethical mandates into actionable constraints can support a shift frompost-hoc ethical critique to continuous design input within such collaborative environments.

Model Predictive Control (MPC) is an advanced control method that utilizes a model to make predictions about the state evolution for the controlled system, as well as an optimization solver to compute an optimal future control trajectory that satisfies state and control constraints. Inaccuracies in model predictions, caused by modelling errors or external disturbances, significantly hinder the performance of MPC and potentially lead to constraint violations. To address these challenges, Robust MPC (RMPC) explicitly accounts for uncertainties in the model, enhancing the control reliability. Tube-based MPC (TMPC) is a well-known RMPC method, where a nominal MPC generates a reference state trajectory, a tube (i.e., a trajectory of model uncertainties centered around the reference trajectory), and an ancillary control law that ensures the actual states of the system remain within this tube —following the reference trajectory, despite uncertainties.

This thesis presents a critical analysis of current state-of-the-art MPC methods, and proposes novel theoretical developments, architectures, and extensions for effective and computationally efficient handling of uncertainties via MPC . These contributions are rigorously supported by formal proofs. Furthermore, the proposed control frameworks are systematically evaluated through dedicated computer-based simulations, with comparisons drawn against existing methods in terms of optimality, robustness, and computational complexity.

The thesis begins with introducing State-Dependent Dynamic Tube-based MPC (SDD-TMPC), an extension of TMPC designed to more effectively handle the variability of model uncertainties and environmental disturbances. By leveraging available information about state-dependent uncertainties, SDD-TMPC enhances optimality and reduces risks of infeasibility, while maintaining the same level of robustness as TMPC. Although SDD-TMPC demonstrates applicability to systems with varying uncertainties across the state space, its practical implementation is limited by high computational demands.

To mitigate this limitation, Approximate State-Dependent Dynamic Tube-based MPC (ASDDTMPC) is developed. This approach employs Spiking Neural Network (SNN) to approximate the behavior of SDD-TMPC. SNNs were selected for their event-driven processing and biologically inspired efficiency, offering significant advantages for low-power, real-time control. Recognizing that this approximation introduces additional uncertainties, and thus the risk of insufficient robustness to uncertainties, the SDD-TMPC framework is extended to incorporate these approximation errors as additional state-dependent disturbances, thereby preserving robustness. The reduced computational requirements of spiking neural networks enable implementation on resource-constrained platforms, such as small-scale robotic platforms.

Next, the Parent-Child MPC (PC-MPC) architecture is proposed to further reduce computational complexity across a wide range of MPC frameworks. Compatible with both tube-based and deterministic MPC, the PC-MPC architecture decomposes the optimization problem into two linked problems: The Parent MPC (P-MPC) addresses long-term stability and constraint satisfaction, while the Child MPC (C-MPC) focuses on short-term stability and disturbance rejection. P-MPC communicates additional constraints to C-MPC, which determines and executes control strategies. This hierarchical approach, which guarantees robustness and stability, is extendable to systems with complex dynamics and large scales. This is done by incorporating additional Parent layers to further manage computational complexity in such systems.

While robustness is critical, there are environments where maintaining strict constraint satisfaction is infeasible due to the nature and extent of uncertainties. To address this, a new theoretical framework called Fuzzy-Logic-based MPC (FLMPC) is developed, particularly suited for controlling multi-agent systems with imperfect environmental perception operating in unknown environments. FLMPC uses fuzzy vectors to model uncertainties, where each element—being a fuzzy variable—represents the degree to which a region exhibits properties such as “dangerous” or “certain”. Fuzzy maps are constructed by grouping fuzzy vectors. FLMPC performs fuzzy optimization to compute optimal trajectories for all agents, demonstrating superior performance and reduced computational complexity compared to state-of-the-art control methods. This efficiency is achieved by enabling the execution of computationally intensive tasks of fuzzy map generation outside the real-time optimization loop. This is made possible by inheriting the fundamental strength of fuzzy logic, particularly its ability to handle uncertainties over a continuum of values, rather than discrete thresholds. This allows for reliable decision-making based on fuzzy maps within a flexible computational window, rather than being restricted to a specific time step.

The inherent limitation of finite prediction horizons in MPC poses a challenge for exploring tasks in large-scale environments. To improve scalability, a bi-level FLMPC framework—leveraging the PC-MPC architecture in the context of FLMPC — is introduced, with potential for extension to multi-level hierarchies. The Parent Fuzzy-Logic-based MPC (P-FLMPC) formulates a global plan using comprehensive environmental knowledge, while the Child Fuzzy-Logic-based MPC (C-FLMPC) focuses on local enhancement and execution of the plan, retaining flexibility for real-time adaptation.

This PhD thesis addresses the optimal control and AI-based control of Search-and-Rescue (SaR) robots. The work is motivated by the need to improve the efficiency of SaR operations after disasters, using robots. In fact, the main benefits of using SaR robots are reduced cost, improved speed of response, increased search performance, extended reachability to otherwise inaccessible places, and fewer risks for the SaR crew. Robots can optimize the mission plans and safely explore the environment through systematic mathematical approaches. Therefore, novel control approaches are needed to enable
robots to perform SaR operations autonomously and time-efficiently, and this is the main objective of this thesis.
The main contributions of this PhD thesis are the following:

1. We propose novel mission planning frameworks and architectures for ground or flying SaR robots based on Model Predictive Control (MPC), Fuzzy Logic Control (FLC), and other control approaches, in some cases combined to exploit the advantages of multiple methods.

2. We integrate our architectures with models for moving targets and dynamic obstacles, and we leverage robust control formulations to deal with uncertainties and perception approaches to map the SaR environment and track targets.

3. We validate our approaches by comparing them to other state-of-the-art approaches in case studies with simulations and in some cases with real-life experiments in the lab.

The need for automation of unmanned aerial vehicles (UAVs) rises with the increase of inexperienced operators. Extensive research has gone into automating and understanding multirotor UAVs and fixed wing UAVs. As vertical take off and landing (VTOL) UAVs are a relatively new category, there is ample opportunity for research in automation and understanding. This report focuses on automating the landing of a specific VTOL UAV: the
Marlyn, created by ATMOS UAV.

Marlyn is a tailsitter, meaning that the centre of mass during the hover phase is relatively far away from the landing rods. This can lead to tip overs during landing. The centre of mass alone will cause a tip over moment at a pitch angle of θ ≤ −32.5 degrees. Experiments have been performed to find the relation between the incoming wind speed and the pitching moment generated by the wind. Adding the found pitching moment, the tip over limit angle decreases to θ ≤ −47.5 degrees. This corresponds to a wind speed of 8.4 m/s.

A surprising find was that not the wind nor the centre of mass was the biggest culprit for tip overs. The biggest factor was the reduction in controllability. Marlyn’s bottom tail motor is in line with the landing rod that first touches the ground. This gives Marlyn no way to counter a forward tip over once it has been set in motion.

The firmware of Marlyn (PX4) comes with a complete simulation environment based on Gazebo. This simulation runs a simple aerodynamic model. The parameters for this model are based on estimations gained from XFLR. To determine and measure the difference between the simulation and reality, a sensitivity study is done. This study shows the influence of the parameters gained from XFLR on the lift and drag forces. After the sensitivity study, the values from XFLR are compared to the experimental data used to find the relation between the incoming wind speed and the pitching moment generated by the wind. The absolute values deviated significantly, but the relative differences between the values was more or less constant.

To counter the tip overs, three strategies are proposed. The first strategy revolves around detecting the moment the bottom landing rod touches the ground. By killing the motors, the wind will always prevent the forward tip over by blowing Marlyn upright. Using the inertial measurement unit inside the autopilot to determine the impact showed to be non feasible given the current design. The impact forces were not significant enough on all surfaces to reliably be used as a trigger to kill the motors. Mechanical switches have been shown in previous research to not work reliably in all conditions, mainly failing in muddy or sandy environments. Range finders and vision based distance sensors showed the most promising, if it can be guaranteed that they always point straight down.

The second strategy is based on generating a moment using the touch down impact to fling Marlyn upright. To generate this moment, a horizontal impact is generated by allowing Marlyn to drift with the wind in a controlled manner. The path generated for this could be done using artificial potential fields. These can be used to safely find the optimal path to the ground while staying within safety bounds. To validate the feasibility of this strategy, a simple inverted pendulum simulation is used. The Gazebo based simulation could not be used as is, since the control loops that try to stabilise Marlyn would try to counter the moment enerated by the impact. The simple simulation has shown that there is merit in this strategy, but a lot of further research is required to validate that this also works in the real world.

The last strategy proposed tries to reduce the pitch angle during landing. This can be achieved via two methods that are discussed. The first rotates Marlyn so that the wings are not tangent but parallel to the wind. This method was discarded due to the actuators not being able to keep Marlyn in that unstable state for prolonged duration. The second method uses Marlyn’s ability to rotate her wing motors independently. By rotating both into the wind, these motors can generate a force to counter the drag caused by the wind. This in turn leads to a smaller pitch angle required to remain stationary. The Gazebo based simulation is used to validate this method’s feasibility. The wind speed at which the previously found tip over pitch angle was crossed was increased from 8.4 m/s to over 9 m/s. Due to instabilities in the simulation, wind speeds larger than 9 m/s could not be tested.

All strategies discussed have promising options. The most promising strategy to counter tip overs during the landing of the VTOL tailsitter UAV Marlyn is: Rotating the wing motors into the incoming wind to reduce the required pitch angle to remain stationary. Further research is required to validate this strategy in real world scenarios

Swarm robotics (SR) is an emerging field of research that utilizes a swarm of robots that work together as a team to solve complex problems. One such challenge in SR is the exploration of unknown environments to find multiple targets. This problem finds applications in various domains, such as archaeology, underground exploration, signal source localization, and more. In line with this, the objective of this thesis is to develop a scalable SR algorithm for efficiently finding clustered targets in large unknown environments. Similar to how artefacts in archaeology are often found clustered around old settlements in large areas.

Inspired by Bee Swarm Optimization (BSO), the proposed algorithm leverages the strengths of BSO in balancing exploration and exploitation. However, modifications are made to adapt the proposed algorithm to incorporate limited communication range scenarios, common in large-scale environments. Also, the exploration-exploitation properties of BSO are redesigned. The proposed algorithm is named Modified Bee Swarm Optimization (MBSO). Here, robots assume different roles (scout, onlooker, experienced forager), similar to BSO, to optimize search and exploitation tasks. To address the issue of limited communication range, the robots establish an ad hoc network, truncating target information throughout the swarm. Additionally, an Artificial Potential Field (APF) is introduced to guide robots towards targets, and away from readily travelled clusters. To further aid the balancing of exploration and exploitation, a swarming architecture is introduced. This architecture is called the Architecture Multi-robot systems heterogeneous robots with Emergent Behaviour (AMEB) and aids with the decision-making of individual robots. The AMEB architecture is used to determine whether scouts should become onlookers, and the speed at which experienced foragers change back to scouts while considering real-robot physical limitations and individual performance levels. This architecture facilitates the continued advancement of the algorithm by allowing for the integration of additional sensory inputs, which in turn influences individual decision-making and, consequently, the emergence of the swarm. Lastly, cluster recognition is added to the algorithm, resulting in robots not transferring to readily travelled areas.

The characteristics of MBSO are evaluated. The target finding performance is benchmarked against a generic random walk method that stems from Lévy walking. Furthermore, this study investigates the effects of scaling the algorithm with an increasing number of robots and varying specific control parameters of MBSO on various aspects such as performance, redundancy, scalability, stability, and robustness. The results of this research have implications beyond archaeology, as the algorithm can be applied to various multi-target search problems in large unknown environments, such as minefield detection. By adjusting the proposed input variables, the algorithm can be optimized for these different scenarios. The developed SR algorithm shows promise in efficiently finding and truncating target information, leveraging the short communication range of the individual robots.

Overall, this thesis presents a novel approach to autonomous multi-target searching, in cases where targets are spread out in clusters, using scalable robotic swarming algorithms. In a field of 0.36 ha with 34 targets spread out over 5 clusters, robots that transfer with a speed of 6.4 km h−1 and optimal parameters for MBSO, scaling from 10 to 15 robots leads to 7.74 % more targets being found. Scaling from 15 to 20 robots resulted in 13.22 % more found targets...

Piping and Instrumentation Diagrams (P&IDs) are graphical representations utilized in chemical engineering plants. Due to confidentiality reasons and legacy drawings, these diagrams are sent in PDF format. Piping engineers need to make a material take-off (MTO), a document containing all the components of a P&ID from these drawings. Today, this is done manually, which proves to be time-consuming and laborious. A piping engineer spends approximately 36 hours per 10 P&IDs, with an average of 500-1000 P&IDs per project. Given the expertise and value of process engineers, this manual counting process incurs substantial costs and a repetitive workload. Consequently, there is a growing motivation to automate this process.

In response, this thesis introduces an innovative deep learning model, PandID-Net, designed specifically for P&IDs. PandID-Net uniquely integrates symbol detection, line detection, and text recognition into a single model, diverging from previous methods that relied on separate models and rule-based techniques. It is the first method that uses deep learning for the line detection task in P&IDs. This all in one approach not only simplifies the processing pipeline but also enhances computational efficiency in detecting and pinpointing symbols, lines, and text, as well as their interrelationships.

The optimal configuration of PandID-Net is found by an ablation study where the performance of individual components is tested in isolation. This optimized configuration is then evaluated and benchmarked against a prior study by Paliwal et al. on the same dataset. PandID-Net achieves a performance in F1 scores of 92.89 and 94.48 for line detection and keypoint detection respectively

Pile driving is a widely used technique for the construction of buildings and infrastructure. A popular technique is to vibrate the pile into the sediment. However, since building sites are increasingly being located in metropolitan areas, there is a growing concern about the environmental impact that vibrations may cause during driving. Therefore, pile driving assessment and prediction have become important for several reasons, including reducing the risk of damage to nearby structures and limiting disturbance to adjacent properties. In particular, pile driveability (i.e., the penetration rate) assessment is immensely useful prior to installation as it increases the construction performance and subsequently reduces costs and environmental impact.

The important factors influencing the penetration rate include vibrator characteristics, pile properties, and soil conditions. However, due to assumptions and the lack of methods that accurately represent the complex phenomena at play during vibratory driving, a disparity is obtained between the predictions of modern pile behavior programs and the observed penetration rate.

Recently, the registration of pile driving data has increased significantly. This extended amount of measurement data can potentially be leveraged for an improvement of the prediction of the penetration rate in future projects. Literature review on the application of machine learning (ML) within pile driving, geotechnical engineering and drilling revealed that the artificial neural network (ANN) is a promising alternative method for the prediction of the driveability of vibratory driven piles (i.e., vibro-driveability).

In this work, machine learning methods and the traditional model were utilized to predict vibro-driveability. Promising ML techniques include the multilayer perceptron neural network (MLPNN) and radial basis function neural network (RBFNN). These neural networks were trained with the particle swarm optimization (PSO) algorithm. The backpropagation (BP) algorithm was also incorporated to train the MLPNN and RBFNN models as a conventional method. Based on results obtained with the aforementioned methods, we propose a new model, the Vibratory Driveability (VD) model, that combines the fruitful characteristics of the MLPNN and RBFNN.

The performance of the five different models was compared with the performance of contemporary vibro-driveability prediction software for three test sets. This was done using different performance indices including the mean squared error (MSE), mean absolute error (MAE) and the weighted average percentage error (WAPE). Additionally, the desired characteristics of the predictions based on the geo-engineer's input were examined and compared with the obtained predictions. It was demonstrated that the ANN-based methods achieved drastic improvements in prediction performance and consequently outperformed the traditional model, making ANN-based methods the preferred alternative for the prediction of vibro-driveability. Among the ANN models, the VD model produced the highest performance, as it reflected the desired prediction behavior for all three test cases and showed competitive prediction performance in terms of the performance metrics.

This work leads to the first-ever published research on the application of artificial neural networks for the prediction of vibro-driveability. As such, it could form the foundation for the development of new (vibratory) pile driving behavior assessment and prediction software. The development of these commercial applications could lead to a considerable reduction in costs and environmental impact.

Labour shortage in the logistic sector and the poor work conditions as a baggage handler are a major and highly relevant problem for airports around the world. A solution is provided by Vanderlande Industies: a baggage loading robot. Since the efficiency of the stacking process is sufficient, operators that overlook the robot must intervene often. To minimise these interventions, better packings have to be made. To make better packings, Vanderlande Industries can adapt the sequence in which the items are presented to the robot. However, literature and the company are not aware of the effects of sequencing. Thereby in this thesis, we conducted research into sequencing items in various ways for the Multiple Container Loading Problem (MLCP). We attempted to find out which methods enhance the fill rate of the packings. In order to understand the academic background of the MCLP, we first looked at the literature. Additionally, state-of-the-art techniques have been investigated. A trip to Schiphol has been undertaken to observe the real robot that is resolving the MCLP as a pilot project for Schiphol in order to put the issue in its proper respective. From this research, we concluded that there are various methods for solving the MCLP, however, sequencing methods applied to so far are mostly kept simple. Moreover, in practice, baggage items are placed in the order where size and weight are decreasing. The knowledge gap found was that sequencing for the MCLP is not researched. We presenttwo different types of algorithms to provide a well performing sequence. The first algorithm, the genetic algorithm, makes use of multiple generations to optimise the sequence. The other algorithm, the heuristic algorithm, makes use of a set of rules. The resulting packings formed with different sequencing methods show to be different. According to the simulation performed, the heuristic sequencing strategy produces the best results for the main scenario in terms of the layer fill rate. The performance of the packings can with the heuristic sequencer be increased by average with 11 % in comparison with packings generated with a random item sequence. Additionally, the heuristic sequencer’s packings are fairly consistent because it consistently manages to place 8 items in one layer of the container. Results in this study point out that sequencing the items with size decreasing performs worse than the packings formed from non-sequenced items, with a mean fill rate of 0.71 for the size decreasing items and 0.87 for the heuristically sequenced items. The alternate scenario produces fewer evident outcomes, but we can point out that simple strategies, such as sorting the items by short side, perform effectively. The results from this applied method: an increase of 7% in comparison with non-sequenced item packings. We assume the simple sorting approach worked well since it grouped all similar shaped objects, resulting in tight packings. The genetic algorithm produced slightly better results, with average outcomes being similar (0.87), but much more consistent packings showing a lower standard deviation of 0.00917 (GA) versus a standard deviation of (0.01747). The results from the heuristic algorithm were the least efficient. This demonstrates that sequencing using a heuristic is effective, but it is not a reliable strategy in all circumstances. The genetic algorithm, in comparison, generates generally worse results, but it can adjust to various situations and configurations. Although the results seem to be convincing, there rise discussion points. One of the major drawbacks is the fact that in this study, simulations are only done in two dimensions. This results in a few assumption regarding stacking behaviour. Thereby, a significant point of future work can be done by introducing the sequencing algorithm to three-dimensional simulations.

Large-scale baggage handling systems, or large-scale logistic networks, for that matter, pose interesting challenges to model-based control design. These challenges concern computational complexity, scalability, and robustness of the proposed solutions. This thesis tackles these issues in a collection of papers organized in two overlapping parts. The first part concerns modeling and Model Predictive Control (MPC) design of large-scale baggage handling systems (BHSs), where a modeling framework for BHSs is proposed that is subsequently used to develop an MPC scheme for control of large-scale BHSs. The MPC controller optimizes for the timely arrival of pieces of baggage at their destination within the BHS network under capacity constraints while minimizing the overall cost of transporting pieces of baggage. Several formulations for the resulting constrained optimization problem are proposed, and they are compared with each other in terms of closed-loop performance and computational complexity. It is shown, via simulation studies, that the proposed solutions can outperform a heuristics-based approach commonly used for control of BHSs while scaling well to larger BHS network instances.

In its second part, the thesis focuses on robustness of control design in the face of a partially known disturbance input (i.e., input baggage demand), and especially on developing a scalable tube-based MPC scheme. For this purpose, considering the BHS model essentially as a linear positive system, a linear-programming-based approach is proposed for the joint calculation of a robustly positively invariant subset and a constrained state feedback controller that minimizes the disturbance-driven L∞ norm of the output over this set. A tube-based MPC control scheme is finally developed by coupling the state feedback controller with a nominal MPC controller, guaranteeing recursive feasibility and asymptotic stability. It is shown via simulation studies that the proposed tube-based approach is effective against unpredictable disturbances. In addition, since the design of both the nominal MPC controller and the state feedback controller involves only linear programs, the proposed tube-based approach scales well to BHS networks of larger size.

Linear positive systems are of interest in several branches of engineering, logistics, biochemistry, and economics. As a spin-off topic and inspired by the applications of the theory of linear positive systems to modeling and control design of systems in the mentioned domains, the third part of the thesis focuses on the reachability analysis of discrete-time linear positive systems. More specifically, we revisit the problem of characterizing the subset of the state space that is reachable from the origin for discrete-time linear positive systems. This problem is of interest in topics such as optimal control of linear positive systems and realization theory of linear positive systems. It is established in this thesis that the reachable subset can be either a polyhedral or a nonpolyhedral cone. For the single-input case, a characterization is provided of when the infinite-time and the finite-time reachable subsets are polyhedral. Finally, for the case of polyhedral reachable subsets, a method, based on solving a set of linear equations, is provided to verify whether a target set can be reached from the origin using positive inputs.

Lane detection represents a fundamental task for automated/autonomous vehicles. Current lane detection methods do not provide the versatility of real-time performance, robustness,and accuracy required for real-world scenarios. The reasons include lack of computing power while being portable and inability to observe the continuity and structure of lane lines over a sequence of images. An investigation into the present methods in the literature reveals that deep learning networks cannot focus on relevant images and critical parts of the images. The neural networks implemented with max-pooling operations can cause a loss of information at a granular level of the image during the downsampling of images. Obtaining a fixed set of lane locations will restrict the number of lane lines detected. It hinders the generalisability of the network. This thesis aims to introduce a novel spatio temporal method that can focus on lane lines and key features to increase the robustness and accuracy of lane line detection.The spatio temporal attention network based on Long Short Term Memory (LSTM) units tested on the tvtLane dataset provided an accuracy of 98.1443%, the precision of 0.8873, and F1-score of 0.9108. The precision and F1-measure are the highest when compared to state-of-the-art lane detection networks. The spatio temporal FC attention network produces better accuracy and precision on TuSimple dataset than state-of-the-art networks with 98.2078% and 0.8861, respectively. Testing on the LLAMAS dataset, the network achieved an average precision of 0.8028 and corner recall of 0.7183. The results show the robustness and high accuracy on two different datasets with unique distributions. Although the network is trained on datasets with a maximum of five lanes, it can detect more than five lanes in an image.The network is also able to detect lanes on the unseen Netherlands dataset.

A satellite remote sensing technique, Interferometric Synthetic Aperture Radar (InSAR), is able to provide surface displacement information on a millimeter level. In this study, data from the TerraSAR-X satellite collected in the years 2009-2018 over the area of Amsterdam is used. Even though radar data is a subject to multi-step processing, there are still several problems observed that can make the interpretation of the time-series difficult for users who are not experts in the radar remote sensing field. In this study we focus on unwrapping errors, partial decorrelation, and incorrectly fitted models. The unwrapping errors are handled as outlier detection problem and the rest as a time-series segmentation task. In order to address these issues, a data-driven approach is proposed. We show a method to detect unwrapping errors based on spatially neighboring points. A GUI is developed to collect expert knowledge in a form of assessing the time-series correctness, marking unwrapping errors, and dividing time-series into separate segments. This information is later used in the evaluation of several outlier detection and segmentation algorithms. We propose a supervised learning approach based on neural networks in order to use expert knowledge. Due to not enough labelled data available, a simulation is developed and used for training of the networks. We present two different approaches, one based on multi-label classification and one on binary classification. For each of them fully-connected neural networks and convolutional neural networks are compared.

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.

A tiny house is a building for permanent human habitation that is specifically designed to have a limited ground surface. The tiny house design discussed in this report has a strong focus on circularity, sustainable material usage, smart systems, and affordability. To achieve an overall self-regulating and ecological concept, the aim is to combine and optimise the different flows that go through the tiny house - i.e. electricity, waste, and water. These flows are also smartly integrated and made more efficient on a network scale. Several tunus tiny houses are combined in a village because sustainable living environments can be created more effectively when collaborating in communities. Eventually, the goal is to obtain a network with such flexibility that its principles can be implemented on any collection of tiny houses or even terraced houses and flats.

The volatility of renewable energy sources pose a significant challenge for Distribution Network Operators (DNOs) as it makes planning and maintaining a reliable electricity grid more complex. An essential tool in dealing with the uncertain behavior of renewable energy resources is the load flow simulation, i.e., the standard electricity network simulation in network design and operation. There is, however, still much untapped potential of applying these kind of simulations. The thesis presents improvements to the theory on linear load flow approximations. The resulting algorithms are then applied to various real world problems: control of a community battery, handling very large simulations, coping with low sensor coverage and evaluating strategic scenario's with high uncertainty. Firstly, theory is presented for the control of a community battery. It is shown how such a battery can be used for grid congestion reduction, backed up by a live experiment. A charge path optimization problem is posed as a linear problem and subsequently solved by an Linear Programming (LP) algorithm. It was found that the voltages and currents can be controlled to a great degree, increasing the grid capacity significantly. Network design formulas are described with which a DNO can quickly estimate the potential (de)stabilizing effect caused by a community battery on the steady-state voltages and currents in the grid. Next, load flow simulations are improved by applying numerical analysis techniques and the accuracy and efficiency of a linear load flow approach is investigated. The resulting fast load flow algorithm is then applied to a very large problem: integrally simulating the low and medium voltage network of Alliander DNO, a grid with over 22 million cable segments with a total combined length of over 88,000 km, built according to international standards. It is shown that this integral simulation can identify voltage problems much more accurately. Next, Bayesian state estimation is considered. A mathematical model is proposed to complement a limited set of real-time measurements with voltage predictions from forecast models. This method relies on Bayesian estimation formulated as a linear least squares estimation problem. The model is then applied to an IEEE benchmark and on a real network test bed. An observability analysis suggests strategies for optimal sensor placement. Next, theory is presented on coping with uncertain long-term scenarios for strategic simulations. A stochastic profile model is proposed based on copulas which can be calibrated by technology adoption data. Using a Monte Carlo approach, the stochastic profiles of all DNO assets are then simulated, identifying parts of the network with heavy loads. Finally, the thesis concludes by demonstrating additional applications of the presented methods, such as fast network capacity checks and reducing losses via network reconfiguration. It concludes by giving suggestions for future research.

The introduction of control systems in the automotive industry has significantly increased safety. Improved control over the vehicle dynamics has been shown to contribute to substantial reductions in the number of deaths and serious injuries resulting from road traffic crashes. The introduction of Electronic Stability Control yielded impressive improvement in vehicle stability. A meta-analysis revealed that a 49% reduction of single-vehicle accidents is realized. Recent research continues the development of a fully autonomously operating vehicle. The vehicle requires the ability to operate in all situations safely, in order to reach the highest level of automation. Vehicle performance should be guaranteed within, at and beyond the limits of friction. Previous research revealed that the unstable drift motion could enlarge the operating envelope of a vehicle. An extensive amount of research is dedicated to controlling a drift. The results show that control systems are increasingly capable of stabilizing a steady-state cornering scenario. The main limitation of these studies is that only a portion of the vehicle motion that is observed in reality can be considered to be steady-state motion.

This thesis presents a multi-objective trajectory optimization which extends the steady-state analysis to a dynamic driving scenario. Based on experimental data obtained with a 1:10 scaled vehicle, accurate vehicle and tire models are derived. It is validated that the models closely mimic the dynamics of the scaled vehicle. In order to justify the use of drifting, the differences between stable and unstable driving equilibria are studied. The stability and controllability are assessed through the construction of the phase portraits and the computation of the Controllability Grammian. The findings, obtained under the assumption of steady-state conditions, are then validated in the dynamic driving scenario. A two-step optimization approach is presented. Spline optimization based on a simplified model is used to obtain initial conditions for a high fidelity model-based optimization. The scope is limited to a single corner, which is optimized under varying velocities and friction conditions.

Under the assumption of steady-state conditions, it is found that the drift motion imposes various benefits over normal driving. Higher cornering velocities and therewith yaw rates can be achieved in a drift. Besides, the principles of tire saturation and force coupling allow for controlling the lateral and yaw dynamics of the vehicle through the rear longitudinal tire force. This increases the maneuverability of the vehicle. The results of the dynamic optimization extend the findings of the steady-state analysis. In the dynamic maneuvers, drifting is found to improve vehicle maneuverability at high velocities and in scenarios of low friction. The approach presented in this work forms a basis for studying the effects that drifting could have on vehicle motion in reality. The relevant aspects of vehicle motion are translated into a multi-objective optimization. The methods that are developed in this work release the simplifying assumption of steady-state driving conditions. As a result, the drift motion can be studied in a more realistic driving scenario. It is expected that through further improving the optimization algorithm, the full operation envelope of the vehicle can be explored.

ZF Friedrichshafen AG is developing its own active suspension system designed as a single unit per corner to optimize the dynamic behavior of a vehicle. The innovative active damper design integrates all the essential components into one damper unit, with the aim to facilitate implementation. This master thesis in cooperation with ZF Friedrichshafen AG presents the development of a disturbance compensation controller for the active suspension unit. The goal is to suppress forces induced by road irregularities on the passenger cabin. The emphasis throughout this work lies on the implementation and applicability of the control algorithm.

The increasing complexity of mechanical systems has resulted in an increased usage and dependence on data driven modelling techniques in order to obtain simple yet accurate models of these systems. Neural networks have emerged as a popular modelling choice due to their proven ability to learn complex nonlinear relationships between inputs and outputs of any given system. Moreover, they are capable of generalizing on data that they have not been trained on. The downside of modelling with neural networks is that they do not provide any insight into the dynamics of the system they model. This limits the application of neural networks in carrying out fault diagnosis of mechanical systems to just the fault detection and isolation (FDI) tasks. While in some applications this may be sufficient, sometimes alongside FDI, it is also desirable to carry out a fault identification task in order to determine the necessary adjustments to bring the faulty system back to its normal operating condition. This thesis explores the possibility of carrying out a fault identification task alongside an FDI task for a mechanical system that has been modelled by a neural network. Traditionally, the weights of a trained neural network represent the strength of a connection between the two neurons they connect. The possibility of an existing correlation between the weights of a neural network and the properties of the mechanical system being modelled is a concept that has not been fully explored yet. This study considers that such a relation exists, implying that the change in certain properties of the mechanical system due to the occurrence of a fault can be related to a change in the corresponding weights of the neural network modelling the system. Consequently, the change in weights of the neural network could give an insight into the fault occurrence in a mechanical system. Taking this idea forward, two fault diagnosis algorithms have been proposed in this study - a fault detection algorithm using adaptive threshold, and a fault isolation & identification algorithm based on sparse Bayesian learning framework. The proposed algorithms were tested on (hypothetical) faulty linear and non-linear systems. The results show that the adaptive threshold based fault detection algorithm was successful in detecting the occurrence of faults in the linear system. For the non-linear system, although a simplistic neural network was used to model the system, the fault detection algorithm was still successful while returning few and sparse false positives and negatives. The fault isolation & identification algorithm was also successful in isolating and identifying all the changed weights in the neural networks modelling the system for both linear and non-linear cases. Although the algorithms proposed show promising results for the experiments conducted, further research is needed to establish the suitability of using them in real world applications.

In the automotive industry automation is popular and every year car OEMs advance their technology to be able to drive autonomously. Longitudinal control of the vehicles is an important part of the complete autonomous driving system. The difficulty of this control problem lies with changing longitudinal dynamics and the lack of full-state system information. This complicates controller design when using classic model-based approaches such as Optimal Control (OC). Currently the controllers are still manually tuned by control engineers in the
vehicle. This is time consuming and expensive, therefore other methods for controller design such as learning are explored. Reinforcement Learning (RL) is one of those methods. To examine the potential benefits of learning a controller, this work will make a comparison between RL and OC. For RL, an actor-critic structure using deterministic policy gradient is applied. Due to partially observable system dynamics OC is used as an optimal output feedback controller. The comparison complies speed control of an autonomous vehicle. The RL agent will learn a controller by training on a nonlinear high fidelity vehicle model. In this work it was demonstrated that RL can reach the same performance as OC when all environmental settings are comparable. When environmental settings deviate, it was is found that RL outperforms OC. To verify the simulated results all controllers were confirmed in an experimental real-life setting.In conclusion, this proved a promising benefit of learning with respect to classical controller computation, when dealing with partially available system information.

One of the most promising ideas in autonomous vehicle control systems is letting the vehicle drive autonomously outside the normal, linear, operating region and letting it "drift". By doing so, the maneuverability of the vehicle could be enhanced. To enable systems that can control this behaviour, estimation of certain vehicle states is needed with high accuracy and high frequency.In this project, a new solution to this problem is proposed by combining a mixed dynamic-kinematic observer with a single camera that produces velocity measurements based on tracking the ground plane. To improve filtering of the camera velocity measurements, the measurement error covariance matrix is updated online based on a model of the camera measurement error. Evaluation of the new methodology was done on data recorded from a 1:10 scale test vehicle and performance was assessed based on ground truth data obtained using a Motion Capture System.In normal driving conditions with correctly identified vehicle parameters, an observer without camera performs better by 25% in terms of RMSE on lateral velocity and sideslip angle estimation. However, the online adaptation of the covariance matrix results in an estimate that is at least 45% more accurate in terms of RMSE than the same observer without online covariance adaptation. Next to that, experiments show that the proposed observer with camera has better robustness to uncertainty in model parameters by almost a factor five in terms of RMSE than the observer without camera.When the grip of the tires is physically lowered and the vehicle is drifting, the proposed observer can track large sideslip angles (>30°), where the state-of-the-art observer without camera is not able. The state-of-the-art observer has an increase in RMSE of 75% on all estimated quantities in comparison to the proposed methodology. These results show that adding a camera to an existing sideslip angle observer greatly enhances robustness of the observer to uncertainty in model parameters and violation of model assumptions. This comes dat the cost of losing some accuracy in normal driving conditions.