With a rising number of airline passengers, airport ground support vehicles are under increased pressure to deliver their services on time. In this thesis, we apply two methods for vehicle routing problems not yet explored for the aircraft ground handling problem: Hybrid Genetic Search (HGS) and Iterated Local Search (ILS) for the aircraft ground handling problem at Schiphol airport, and compare them against an established method. We show that both HGS and ILS are capable of solving realistic instances within a realistic amount of time available were such an algorithm to be deployed. We also show a method to revise a schedule in the case of disruptions and show this method can easily be performed without large changes to the solver. Our results also show ILS to perform better on small instances and HGS to perform better as the problem difficulty is increased. This thesis shows HGS and ILS are well suited to be used for airport ground support vehicle scheduling at airports, and can be used as a basis to build these systems. It provides algorithms which could be used in these systems, also in case of scheduling disruptions. This thesis can serve as the starting point for further investigating the usage of HGS and ILS, now that their effectiveness has been shown, using potentially new search methods. We also highlight other potential research avenues in comparing the results for airports of different shapes and sizes and with additional constraints, which may be explored using the methods in this thesis as a basis. ... Read more

Informative path planning (IPP) tasks UAV with locating and accurately measuring high concentrations of specific phenomena in initially unexplored, budget constrained search space. Adaptive-IPP closes the loop between sensing and planning by replanning sensor trajectories as a probabilistic belief about the environment evolves but even state-of-the-art planners face two challenges while replanning their trajectory online: 1.) Replanning during deployment incurs substantial online computation and thus time costs during deployment, which is problematic in a time-budgeted mission and 2.) the resulting path-optimization landscape is non-convex, producing a fundamentally multimodal set of valid solutions. Behaviorally cloning such an expert with a deterministic, MSE-trained policy mode-averages these equally valid trajectories, producing plans that correspond to none of them and fail to learn the underlying monitoring objective in unseen environments.This thesis presents a path-planning framework that instead uses a conditional denoising diffusion model to generate valid 3D waypoint sequences for continuous environments. A novel CMA-ES-based data collection procedure exposes multiple deployable expert trajectories per belief state, giving the diffusion model a genuinely multimodal demonstration set to learn from. We tested our diffusion based model in simulated missions on synthetic Gaussian random fields and real-world agriculture maps based on the National Agricultural Imagery Programme (NAIP) and found that diffusion reduces time-integrated map uncertainty by roughly 34\% relative to the online replanning expert that it was trained on, while replanning approximately 11× faster by appropriately learning the goal of online replanning through the multimodal dataset, and consistently outperforms a deterministic learning baseline trained on the same demonstrations. These results suggest that diffusion-based behavioral cloning preserves valid planning modes and enables real-time, non-myopic informative path planning. ... Read more

The hexapod motion system is the industry standard for full-motion flight simulators. However, specialized applications may require more complex motion systems. Equipping a hexapod with an additional yaw-drive on top would offer the degree of freedom required in evoking spatial disorientation. This study aimed to develop an unconstrained yaw extension to the Classical Washout Algorithm that fully utilizes the capabilities of this motion system by accurately compensating for any yaw-drive orientation without degrading the translational and rotational cueing fidelity. The Classical Washout Algorithm was extended through the addition of a Yaw-drive Channel to cue the low-frequency components of yaw. Furthermore, the kinematic relations between the body and the inertial frames of reference were reconstructed to support any yaw-drive orientation. The design followed a two-step approach using a tradeoff based on objective metrics, followed by a subjective evaluation with a pilot-in-the-loop experiment. From the experiment, a limitation of the Unconstrained Yaw Washout Algorithm was identified. The first version presented in this paper was able to incorporate unconstrained yaw and correctly compensate for any yaw-drive orientation. However, by prioritizing yaw-drive compensation, surge/sway and pitch/roll stimuli became convoluted, and control over the individual stimuli was lost. To address this, the filter parameters for the Translational and Rotational Channels were harmonized, leading to the final design presented in this paper. Collectively, the insights gained from the design process have formed three alternative implementation recommendations aimed at compensating for the yaw-drive while regaining control over surge/sway and pitch/roll cues. ... Read more

The continued growth of air traffic demand is placing increasing pressure on current air traffic control (ATC) systems, prompting the need for alternative ATC strategies. A promising approach is a shared ATC environment between a human controller and an automated controller, where basic, low-complexity traffic is delegated to automation while complex traffic remains under human control. This concept requires a reliable method for predicting the operational complexity of individual flights. This research presents the design and evaluation of a complexity-based flight allocation algorithm for an en-route shared human–automation ATC environment. The allocator classifies incoming aircraft based primarily on the predicted number of interactions along their trajectories, using a flight-filtering mechanism derived from existing models. Additional allocation metrics include the expected number of interactions between human and automation-directed flights and a minimum number of flights controlled by each controller. The allocator was evaluated using offline simulations with real traffic data, followed by a human-in-the-loop experiment with two professional air traffic controllers. Results show that the allocator can consistently assign more complex flights to the human controller while maintaining a balanced workload distribution. The human-in-the-loop experiment saw substantial manual re-allocation and revealed low trust in both the allocator and the automation, indicating the need for further refinement and closer integration with automation capabilities. ... Read more

Sample efficiency is a critical metric in intelligent control systems as it directly influences the feasibility and effectiveness of learning-based approaches. This paper presents the study of how Randomized Ensemble Double Q-Learning (REDQ), a sample-efficient model free algorithm, can be used in flight control applications. Three controllers were developed for: pitch, roll and combined biaxial attitude tracking tasks and tested on a high fidelity Cessna Citation 550 model. For each control task, three agents were trained offline: two using REDQ enhanced Soft Actor Critic (SAC) architectures and one using a standard SAC architecture for comparison. REDQ agents showed statistically significant improvements in sample efficiency during initial learning. Average accurate tracking convergence (error < 1◦) occurred within 5,500 training steps for pitch, 6,400 for roll and 11,500 for biaxial control. The gains in sample efficiency were shown to have drawbacks in learning stability and robustness when deviated too far from nominal conditions. ... Read more

This thesis develops a mathematical optimization model to optimize charging schedules and energy management for electrified aircraft at Rotterdam The Hague Airport (RTHA), addressing research gaps in adapting airport infrastructure for electric aviation. Based on a real-life flight schedule from 2019, the model determines a Battery Energy Storage System (BESS) size while minimizing operational costs, such as grid electricity, photovoltaic (PV) use, BESS degradation, and flight delay/cancellation penalties, while trying to maintain the schedule as closely as possible. Five electric aircraft types, ranging from a 2-seater flight school aircraft, to a 90-seater commercial aviation model, were considered with a Constant Power - Constant Voltage (CPCV) charging profile, alongside a detailed mission energy analysis. Seasonal simulation for January, April, July, and October 2019 showed delays averaging from 2 minutes in July while peaking at 45 in January, alongside three flight cancellations due to high energy demands. Optimized BESS sizes range from 7 MWh to 12 MWh. Optimization of the model showed a reduction of up to €500,000 weekly when compared to a baseline case. Sensitivity analysis showed that increasing the grid import limits from 3.5 MW to 5 MW gave better grid reliability, with less delays and cancellations, while a decrease to 2 MW showed increases in delay times and cancellations. When the export limit was reduced from 7.5 MW to 5 MW, the delays increased due to constrained energy offloading, while increasing it to 10 MW decreased the delays and cancellations by one. A 1 MWh BESS increase reduced cancellations by one and total delay time by up to 5 hours. Adjusting the turnaround times by ± 15 minutes demonstrated the model’s resilience to stricter turnaround times, but a 4-hour delay increase was present with extensions. The findings of the thesis show the critical role that BESS capacity and grid limits play in ensuring operational efficiency for the electric aviation infrastructures of the future, but also the cost and delay reduction by optimizing charging schedules. ... Read more

Scarce failure data often causes unreliable results when making predictions concerning Remaining Useful Life (RUL). This study explores the use of deep generative models (DGMs) for augmenting turbofan engine datasets by CMAPSS to improve these RUL predictions. By implementing Conditional Tabular GANs (CTGAN) and Tabular Variational Autoencoders (TVAE), synthetic data is generated and validated using statistical metrics such as Wasserstein distance and Kolmogorov-Smirnov tests. Then, these new datasets are used in several compositions of both real and synthetic data to train regressors and subsequently let them make RUL predictions. The regressors, such as Random Forest Regressors (RFR) and Convolutional Neural Networks (CNN), evaluate performance improvements through RMSE and MAE metrics. Results indicate that adding synthetic data improves prediction robustness, particularly when data is limited. This highlights the potential of DGMs for Prognostics and Health Management (PHM) applications. ... Read more

This study addresses the initial phase of a multi-modal air cargo transport network, where trucks collect shipments from multiple origins and deliver them to the hub airport of an airline. Efficient coordination between ground transport and outbound flights is crucial for optimising truck load factors, reducing operational costs, and ensuring on-time cargo transfers at the hub. Poor synchronisation can cause delays and increased expenses, reducing the efficiency of the entire transport network. This paper presents a novel Mixed Integer Linear Programming (MILP) formulation and an Adaptive Large Neighbourhood Search (ALNS) framework for an integrated vehicle routing and dock-door scheduling problem that includes split delivery, incompatible products, time windows, and open routes, with the objective of minimising operational costs. The ALNS framework uses a dock-door-based route representation along with multiple insertion and removal operators to improve the solution to the problem at hand. A comparative analysis between the MILP and ALNS model shows that the ALNS model consistently outperforms the MILP model in computational efficiency and solution quality for larger and more complex instances. The ALNS model efficiently finds feasible solutions within significantly reduced computational times, making it practical for real-world applications. Moreover, using a case study of an airline, the ALNS-generated network demonstrates improvements in cost efficiency, fleet utilisation, and truck load factors compared to the airline’s historical routing data. Despite differences between the actual network data and the model-generated data, stemming from assumptions that create an idealised scenario that does not fully capture the complexities of real-world operations, the ALNS model offers significant enhancements in efficiency for the airline’s trucking network. ... Read more

This study addresses the operational challenges of sustainable aircraft towing at hub airports, partic- ularly focusing on taxi delays. Using a queue-based discrete event simulation, airport taxi operations are modeled to evaluate the impact of tow-trucks on taxi delays and runway throughput. Key parameters in- clude service times for coupling and decoupling, tow-truck availability, and parallel service capacity. The findings reveal that taxi delays and runway throughput are significantly affected by the mean and variability of service times. Allowing multiple parallel service stations in front of the runway can reduce delays but may require infrastructure modifications. The study concludes that while operational towing can reduce emissions, its successful implementation depends on careful management and optimized traffic strategies to avoid compromising airport efficiency. ... Read more

This thesis presents the development process of an aircraft control law. The control law is designed using a two-degree-of-freedom (2DoF) structured H∞ loop-shaping approach. This method allows the reuse of controller structures required by certification procedures while
directly including handling qualities and robust stability requirements in the optimization process. This strategy is employed to develop a Rate Command and Attitude Hold (RCAH) demand system aimed at satisfying longitudinal handling qualities. First, the stability of the
open-loop model and its compliance with the handling qualities guidelines are evaluated. Then, the control law is designed. In this step, a detailed description of the design specifications and how to specify them in the context of H∞ control is given. Subsequently, the controller parameters are optimized to satisfy the design specifications and a closed-loop analysis is performed. Finally, a simulator flight testing campaign is conducted to experimentally validate the designed control law. It is shown that the aircraft equipped with the RCAH system achieves better handling quality ratings (HQRs) and more favorable pilot feedback, providing a substantial improvement over the bare airframe. ... Read more

The relationship between energy consumption and baggage handling systems (BHSs) has not been widely studied. In this study, this relationship is explored by considering BHS configurations - the designed arrangement of devices. The BHS consists of seven main processes: drop-off, transportation from drop-off to screening, hold baggage screening (HBS), transportation from screening to sortation, sortation, early baggage storage (EBS), and make-up. Transportation appears to cause a high share of the system's energy consumption, as it is a part of and connects several processes. An equation-based model is developed to estimate the energy consumption of BHSs with varying configurations. The model only contains parameters available at the BHS's conceptual design phase and is based on the BHS of a midsize airport in Scandinavia. It includes a series of formulas for each process, which depend on the structure of the process elements. The study proved a twofold effect of an airport’s BHS configuration on the overall energy consumption. Firstly, more process elements structured in series result in bags travelling a longer distance on the conveyor within the BHS, thereby increasing energy usage. Secondly, the parameter values for transportation impact energy consumption notably. The research suggested that more process elements structured in series decrease the overlap of device usage, which in turn reduces energy consumption. ... Read more

The usage of drones in urban environments is expected to grow rapidly in the coming decades. To ensure the safe operations of drones, conflict detection and resolution are vital. Currently, a lot of research has gone into state-based CD&R, which has proven effective in unconstrained airspace but suffers from a large number of false positive conflicts in constrained airspace. The use of intent in constrained CD&R has the potential to reduce the number of false positive conflicts and improve the safety of drone operations significantly. In this paper, an intent-based detection and resolution method for orthogonal constrained very low-level urban airspace is presented and evaluated against a state-based method. The intent-based method calculates the future position along the trajectory at a time interval of 3 seconds for each aircraft, and conflicts are then detected by comparing these positions. The conflicts are solved utilizing a rule-based algorithm. The results show that the intent-based method has a much lower false positive rate for all traffic densities, as well as a higher average detection time before conflict for larger look-ahead times compared to the state-based method. The resolution of the state-based method, however, shows better performance with fewer losses of separation occurrences. With improvements, the intent-based method's low false positive rate, combined with the use of a larger look-ahead time, allows conflicts to be detected more reliably and earlier than the state-based method, thereby facilitating earlier conflict resolution and enhancing safety. ... Read more

In Haptic Shared Control (HSC), human-like reference generators and adaptive strategies have shown promising potential for minimizing human-machine conflicts, though these advances have thus far been limited to simple control tasks. This paper extends the application of low-conflict HSC to a more realistic driving task. An Adaptive HSC (A-HSC) design for constant-velocity steering under changing visibility is proposed, where the A-HSC dynamically adjusts its steering support to align with the human driver's behaviour. In a human-in-the-loop simulator experiment with 16 subjects, the proposed A-HSC adapted successfully to the drivers' steering behaviour, converging to an average look-ahead time of 0.46s in low visibility and 1.01s in high visibility. When visibility decreased during the task, the driver's trust and control authority influenced the adaptation, with half the drivers complying with the A-HSC support allowing it to retain high-visibility settings. The A-HSC outperformed the fixed low-visibility HSC system in reducing driver control effort, minimizing conflicts and improving subjective acceptance. However, it ranked lower than the fixed high-visibility HSC overall. To further enhance A-HSC adaptability and driver acceptance, future research should investigate how varying the authority balance between the HSC and the driver affects adaptation, and explore more intuitive HSC structures, potentially based on the driver's visual aim point. ... Read more

Increasing runway capacity is a key objective for many airports worldwide, including Schiphol Airport. One of the restricting factors that has become more prevalent throughout the years lies in the task load of the Air Traffic Ground Controllers. To overcome this limiting factor, it becomes essential to mitigate this high task load. This study proposes a methodology for estimating ground controller task load at Schiphol Airport, which is used for analyzing the effectiveness of a mitigation method employed at Schiphol Airport, namely the splitting of the combined North-Centre sector. To achieve this, the research adopts the concept of task load estimation via dynamic density modelling, which has primarily been explored when applying it to the airborne segments of Air Traffic Management. The task load estimation models for the different ground control sectors are developed through a multi-phase approach encompassing data collection, candidate independent variable selection, a double correlation analysis, and a sequential search regression for model building. The research successfully constructed task load estimation models for four out of the five analysed sectors. Furthermore, two of these models are employed in a case study to evaluate the task load reduction strategy. The findings highlight that splitting the combined North-Centre sector to alleviate the task load experienced by the North sector ground controller can be an effective mitigation strategy, particularly when the Centre sector experiences a significant level of traffic load. ... Read more

One of the current trends in the aviation world is to work towards an increasingly more computer-aided approach to flying. Despite the improvements, limitations still inevitably exist in terms of power and storage capabilities in the aircraft avionics. To overcome this problem, different solutions have been proposed. A data-driven approach is implemented in this work on a practical application of aircraft performance function. Within the function, the aerodynamics and propulsion submodels are the target of the reduction activity. Neural networks and other surrogate model structures are tested and evaluated on the use case. Notably, several different network architectures are implemented in order to investigate a set of trade-offs between approximation accuracy and model complexity. An analysis of the error introduced by the model approximation is carried out to evaluate the impact at global functional level. ... Read more

It has been shown that expert gaze behavior can be used to enhance training for novices, among others in the field of laparoscopic surgery. The research in this paper focused on the effect of changing pilot gaze behavior on flight performance and detection task performance. Two groups of novices were shown a recording of expert behavior, including an indicator of the expert's gaze. The actively trained group was additionally shown a gaze director during training, to suppress the field of view outside of a set gaze point. Said gaze point moved around in a basic scanning sequence, encouraging this scanning pattern in the participants. Both groups were then evaluated on flight and detection performance in a segment of straight level flight, during which objects around the aircraft had to be detected. The \ga showed a larger change in gaze behavior between the before and after training tests, accompanied by a reduction in flight performance. Detection task performance was comparable for both groups. The gaze director thus seemed to distract participants from the tasks. One suggestion for future improvement is to focus on tuning the gaze director's timing using questionnaires. Alternatively, further research could focus more on the passive exposure of novices to expert gaze behavior, and compare this to a control group. ... Read more

The haptic feedback signal in haptic interfaces is usually in the form of a force on the control device. In contrast, the “active side stick”, investigated in the 80s and 90s, uses an admittance display, whereby the position of the device is linked to the feedback signal, and force applied on the device is used as the control signal. These devices are usually tuned in a serendipitous manner. To better understand the potential and tuning rules for these devices, tuning is investigated with a model-based approach and verified in pilot-in-the loop experiments using various aircraft dynamics. It was found that certain gain settings offered considerable benefits in terms of tracking performance as well as the control effort exerted by the pilot, while taking into account the system's stability margins. Based on these findings, a comprehensive tuning procedure is proposed for control systems involving an active manipulator. ... Read more

Since the first cargo flight in 1910 (Morrell [42]), air cargo has proven to be of great value to society. With over US $6 trillion worth of goods being transported through air cargo, accounting for around 35% of all global trade measured by value (Boeing [15]), it is impossible to deny its significance in today’s world. Air cargo provides us with a fast, reliable and safe method of transporting goods, making it attractive to industries where short shipping times are of the essence. Generally, there are two conventional ways of transporting air cargo – through the use of full-freighter aircraft and through the use of the belly space under the main cabin on passenger aircraft... ... Read more

One of the challenges in executing an MDO system is the selection of the best performing MDO architecture in terms of computational effort for a given MDO problem. To address this, several comparative studies have been created, which analyze the relative performance of MDO architectures with respect to internal features of MDO problems, such as the number of design variables and nature of interdisciplinary coupling. However, the existing research is limited in applicability. An inclusive prediction model, that can recommend the best MDO architecture for any MDO problem is not found yet in literature. In this thesis, the intention is to conduct a thorough comparison of two commonly used MDO architectures with respect to a database of sequentially generated MDO problems, having a range of internal features. Following this, a prediction model of MDO architecture is to be developed by applying a suitable machine learning algorithm on the generated database. ... Read more