As aircraft propulsion is transitioning to hydrogen fuel-cells, ensuring safe and reliable operation remains a key challenge. This thesis develops a control methodology to automate the startup and shutdown of a multi-stack hydrogen fuel-cell propulsion system for aircraft, whilst minimizing stack degradation. The work was conducted in collaboration with DLR, with contributions from Airbus, ZAL and HSU, focusing on air-cooled, open-cathode PEM fuel cells for aerospace applications.

Control-oriented models were developed using Multi-level Flow Modelling, Finite State Machine, and subsystem physical models, which were validated against experimental data in Simulink. Based on these models, subsystem controllers were designed to operate under the supervisory Finite State Machine automatic controller. The simulation results verify the defined control safety and reliability requirements, demonstrating that the multi-level control methodology is robust in automating the startup and shutdown operations, highlighting the use case in future aircraft fuel-cell propulsion systems.

Advances in aerial refueling automation are expected to reduce operators’ manual boom refueling control skills while remaining insufficiently reliable across all operational conditions. This highlights the need for intuitive interfaces that support effective human intervention. The use of artificially generated haptic cues has proven effective for enhancing human manual control performance. This study investigates the use of continuous haptic feedback based on the state of the controlled element in a realistic two-dimensional aerial refueling boom attitude-control task. For this research, two sticks using a command shaping filter, of which one with haptic feedback, were compared to a conventional implementation. Haptic feedback is implemented by shifting the neutral position of a stick, with otherwise conventional mass-spring-damper characteristics, proportional to the boom's rotational rate. It was found that, compared with non-haptic implementations, the proposed haptic configuration improves tracking performance by 35-50\% without increasing operator control effort. Frequency-domain analysis suggests that the implemented haptic feedback allows for more effective control by redistributing the workload and thereby supporting the operator in managing challenging controlled element dynamic behavior. All participants indicated a preference for the haptic stick implementation. The results indicate that continuous haptic feedback is a highly effective means of supporting operator manual control of aerial refueling boom attitude, particularly as automation increases. Incorporating continuous haptic feedback derived from the controlled element’s state is therefore recommended to enhance human manual control performance in both current and future automated refueling operations.

Currently, a high workload is experienced by executive controllers operating in Area Control Center (ACC) Sector 3 of Dutch airspace. Due to a lack of predictive information prior to departure, the planner controller is unable to manipulate regional outbound traffic with the aim of reducing the experienced workload of the executive controller. The main contribution of this paper is the introduction of a decision support tool (DST), that supports the planner controller in manipulating regional outbound flights. The DST incorporates an adapted version of the Luchtverkeersleiding Nederland (LVNL) Workload Model (WLM), which is currently used by ACC supervisors for long-term workload management for executive controllers, to compute workload scores that support the planner controller’s decisions on the timing of take-off clearances for regional outbound flights. Within this research, only the postponement of take-off clearances was considered, as departures can be delayed when necessary, whereas advancing a departure time is often operationally infeasible. An experiment was conducted involving one planner controller and five executive controllers to explore the effect of DST use by the planner controller on the workload experienced by the executive controllers. Overall, the use of the DST demonstrates potential to reduce the workload experienced by executive controllers; however, further adaptations to the WLM, such as incorporating aircraft performance characteristics into the workload calculation, may enhance the effectiveness of the DST for medium-term planning in multi-airport environments.

Enabling real-time identification and detection of changing pilot and controlled element (CE) dynamics in manual control tasks is essential for the development of real-time adaptive support systems (i.e., a “R2D2”) that 1) monitor closed loop dynamics and 2) detect irregularities therein. This study developed a method for identifying CE dynamics in compensatory target-tracking and disturbance-rejection tasks with time-varying pilot and CE dynamics. The CE dynamics were modelled using autoregressive exogenous (ARX) models, with the model parameters estimated using Ordinary Least Squares (OLS) and Recursive Least Squares (RLS). The effects of disturbance power, pilot remnant intensity, and RLS memory horizon on estimation bias and quality fit are studied using Monte Carlo simulations with 100 realizations. The simulation results are verified against the calculated least-squares best-fit model. A memory horizon of 512 samples (5.12 s at 100 Hz) was found to be optimal for all simulated configurations. The results show that for pure target-tracking tasks, a properly selected ARX model structure and an appropriately chosen RLS memory horizon allow for perfect model identification. In contrast, for pure disturbance-rejection, the identification results show the opposite. Increasing pilot remnant intensity was found to have a positive effect on both the mean parameter biases and quality of fit. With this work, an in-simulation functional approach for CE dynamics identification is established, together with a procedure for obtaining the least-squares best possible model fit for the CE ARX model, thereby providing a reference for the development and verification of real-time CE identification methods that, alongside real-time pilot dynamics identification methods, are necessary for the development of the monitoring functionality envisioned for R2D2-like systems.

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.

Pilot manual control behavior has traditionally been modeled using purely visually driven quasi-linear frameworks. The role of proprioception and manipulator feedback (force and position) in shaping pilot dynamics for equalization has been theorized in literature, however well-grounded models that check all boxes remain elusive. This project investigated the feasibility of proprioceptive equalization utilizing a mechanical model of the neuromuscular system (NMS). Frequency domain analysis and root locus methods reveal that muscle spindle and tendon feedback may affect characteristics near the region of crossover, but cannot independently achieve the integrator-like open-loop characteristics required for effective equalization. A mixed equalization strategy based on combining force feedback with visual compensation is shown to be physiologically plausible and theoretically effective in reducing pilot effort. Qualitative comparison against experimental trends supports the conclusion that proprioception contributes to neuromuscular stabilization and performance enhancement; however, it could not conclusively prove the possibility of equalization.

Current approach control at Schiphol is mainly based on radar vectors, which offer high flexibility to the air traffic controller. New technologies such as Required Navigation Performance - Authorization Required enable fixed approach routes with curved segments to be flown with high precision. While fixed approach routes are desirable to reduce track miles, to enable continuous descents, and to avoid noise-sensitive areas, decision support is currently only available on the final approach leg whereas earlier support is needed. The shift to fixed routes requires a decision support tool to merge different approach types without increasing the workload. In this paper, the design of the Final Merge Tool (FMT) is presented, which combines projections of downwind traffic with separation markers on the final approach leg. It uses a time-based prediction algorithm and integrates the projections in the existing support tool. The FMT interface is evaluated in a first exploratory real-time simulation with four professional air traffic controllers from the Netherlands Air Traffic Control, comparing different mixes of traffic on fixed routes versus vectored traffic. Results from this evaluation show that the number of commands issued decreased with the tool and that the subjective workload was lower. Controllers were able to use their own strategies with the tool and generally found the support helpful for determining the sequence on final approach. There were no indications that the tool decreased safety, but further research is needed to confirm this with more certainty.

The identification of time-varying human operator (HO) dynamics is critical for advancing adaptive support systems in manual control tasks. This study evaluates the performance of Adaptive Model Selection (AMS), a framework extending recursive ARX identification methods, for estimating time-varying HO parameters, particularly effective time delay (τ(t)). Two configurations of AMS were tested: AMS_q, employing the z-domain ARX representation, and AMS_δ, utilizing the delta-domain ARX representation.

A Monte Carlo simulation framework was used to simulate a compensatory manual control task under varying conditions, including remnant noise (P_n) and dynamic transitions in system parameters. Stability and convergence rates of delay estimates were analyzed for different window sizes (W_s). Results show that the correlation between W_s and convergence time was linear and remained unaffected by remnant noise, demonstrating that window size is the primary determinant of responsiveness. Larger Ws improved stability but introduced tracking delays, whereas smaller W_s allowed for faster adaptation to dynamic changes at the cost of increased sensitivity to noise.

The comparative analysis between the configurations revealed a strong dependence of delay estimation accuracy on the precision of the natural frequency estimate of the neuromuscular system (NMS). The natural frequency estimation directly influences HO dynamic response modeling, and inaccuracies in this parameter propagate through the recursive identification process, affecting the reliability of delay estimates.

These findings underscore the critical role of window size and natural frequency estimation in determining the accuracy and stability of effective time delay estimation through AMS. This study provides a foundation for refining AMS to better balance stability and responsiveness in estimating time-varying HO dynamics. Such advancements can facilitate more accurate modeling of time-varying HO behavior, deepen understanding of HO adaptation, and contribute to the advancement of adaptive support systems.

Handling high workload is a key concern when implementing Reduced-Crew Operations (RCO). Research has shown that both checklist completion time and decisionmaking performance suffer when reducing the crew complement from two to one. Although automation has historically been used to address workload issues, it has introduced its own set of challenges. Therefore, allocating more tasks to automation with the aim to lower workload may amplify adverse side effects instead of solving any. Instead, automation should be designed to increase the performance of the human-machine system as a whole.

RCO presents an opportunity to critically reassess automation on the flight deck by redefining the role of the pilot. Many researchers agree that the pilot remains the ultimate decision-maker and is responsible for ensuring the safety and success of the flight operation. The pilot’s role will encompass flight planning, communication, and surveillance, while system management tasks are considered suitable candidates for automation. However, automating system management may lead to diminished system state awareness, potentially compromising flight plan management performance. Consequently, additional support is needed to keep the pilot actively engaged with flight plan management tasks.

In addition to addressing the potential adverse effects of automating system tasks, the current support for flight plan management requires already a significant improvement. A key challenge in handling non-normals lies in assessing and integrating disturbances into the flight plan. Pilots must gather, combine, and analyze environmental and system information. This information is often fragmented across multiple sources and requires decryption to become actionable. This process heavily relies on the pilot’s initiative and experience, increasing the risk of unconsidered impacts.

This study examined the impact of elevating the Level of Automation (LOA) for system and flight plan management functions. A proposed concept elevated the LOA of the system management support, specifically the action execution stage from a stepby- step action support to a system that autonomously performs a sequence of actions after human activation. In flight plan management, the information acquisition and analysis stages were highly automated, with the goal of reducing workload while enhancing decision-making performance…

Unexpected in-flight events can trigger startle and surprise, which could impair pilots’ performance but remain difficult to measure. This dissertation addresses this gap by developing and validating self-report instruments to quantify startle and surprise in an aviation context.
Grounded in cognitive models, real-world incident analyses, and robust psychometric methods, the Startle and Surprise Inventories (Startle-I; Surprise-I) and Visual Analogue Scales (Startle-VAS; Surprise-VAS) are introduced and evaluated. Results from multi-phase studies involving field experts and professional pilots, provide strong evidence of validity and reliability.
The findings offer a scientifically validated framework for assessing pilots’ responses to unexpected events, with broad implications for human factors research, evidence-based training, and safety-critical operations.

This study explores the application of cognitive work analysis (CWA) and ecological interface design (EID) in the development of a novel display system for a Dash 8 Q300 aircraft retrofitted with a hydrogen-electric fuel system. By leveraging CWA and EID, this research aimed to address the challenges of managing cognitive complexity in next-generation aviation systems, focusing on designing interfaces that enhance pilot decision-making and situational awareness. These analytical frameworks informed the design process, ensuring that the displays were tailored to the cognitive demands of the pilots. To validate the effectiveness of the proposed designs, interviews were conducted with a regional commercial pilot, an airworthiness engineer, and a test pilot. These interviews provided qualitative insights that confirmed the applicability of the CWA/EID-based designs, particularly emphasizing the need for simplicity and clarity in time-constrained regional operations. The study highlights the importance of focusing display content on critical and abnormal conditions to reduce cognitive load, aligning with rule-based behavior (RBB) frequently employed by pilots. Future work should involve controlled human-in-the-loop experiments with a larger participant pool to empirically test the proposed display designs.

Recurrent training is crucial to helping pilots maintain or upgrade their flying skills. However, constantly monitoring and scrutinising pilots during training can lead to performance pressure. If pilots who are under pressure feel that their behaviour is affected it may ultimately reduce the effectiveness of recurrent training. To study the effects of performance pressure on pilot behaviour and learning, pilots (n=17) holding an airline transport pilot licence and type rating for large multi engine aircraft were invited to participate in a simulator experiment where they would learn a new autopilot system. The low-pressure group flew a series of training scenarios under no performance pressure whereas the high-pressure group flew the same training scenarios with prompts designed to induce performance pressure and raise their anxiety. It was hypothesised that the high-pressure group would be less incentivised to explore different functions of the autopilot due to the performance pressure, therefore leading to a lower quality of training which would be reflected in poorer knowledge of the autopilot system. Ultimately, whilst the high-pressure group did in fact undergo training with a significantly higher state anxiety. This was found to have no significant effect on their behaviour during training or their performance in a test scenario. The findings also suggest that the experiment would benefit from being repeated with more guidance given during training and more constrained scenarios to reduce the variation in the data.

The aviation industry's shift towards sustainable propulsion, notably hydrogen-electric propulsion (HEPS), underscores the need to grasp its effects on flight procedures and pilot duties. This research aims to develop a HEPS engine powertrain model for evaluating its influence on flight procedures, especially during critical phases like go-around for regional turboprop aircraft. The HEPS engine powertrain model was constructed by selecting powertrain components important for modelling based on their relevance to important HEPS system outputs like the thrust generation, battery discharge, and fuel left in the tank. Subsystems such as the fuel cell system, battery, motor, propeller etc. were modelled using MATLAB/Simulink and integrated to form a holistic representation of the HEPS engine. The model underwent testing through a simulated go-around mission, followed by an analysis of its operational envelope, which unveiled significant parameters influencing system behaviour during such manoeuvres. Among the critical parameters identified were the initial battery state of charge (SoC), nominal battery capacity, and propeller efficiency. Thrust generation limitations were identified for heavier aircraft configurations, as well as the relatively low impact of go-around speed on the system's power path. For a HEPS-based Dash 8 Q-300 aircraft with a mass of 38,000 pounds and a go-around speed of 96 knots, the State of Charge (SoC) of a 200Ah battery should be at least 23% for a successful go-around manoeuvre. Using appropriate parameters, the presented model framework assists in identifying such constraints and delineating flight procedural changes for future regional aircraft. The study highlights areas for model enhancement, including better thermal management system models for the fuel cell stack and battery, and improved motor-propeller models. This research contributes to sustainable aviation, guiding the integration of HEPS into flight operations for enhanced efficiency and eco-friendly air travel.

The human operator's manual control behaviour under time-invariant conditions has been successfully modelled. However, significant gaps remain in understanding and modelling adaptive manual control behaviour under time-varying conditions. One such less-understood aspect is the pilot's ability to detect changes in time-varying controlled element dynamics. This study aims to develop a mathematical model that investigates open-loop stability as a criterion in compensatory and pursuit tracking tasks to model the pilot's detection of change in controlled element dynamics across different transition rates, particularly for transitions from stable to less-stable vehicle dynamics. The model operates under the assumption that trained human operators track statistical properties of tracking task signals within periods of compromised open-loop stability to trigger their detection of change in dynamics. The model identifies regions of reduced stability and simulates the tracking task signals through time-varying computer simulations. Subsequently, human-in-the-loop experiments are conducted to validate the model. The validated model demonstrates a combined accuracy of $88.54\%$ for the compensatory task and $80.62\%$ for the pursuit task. Notably, in the experiments, the error signal consistently outperforms the error rate signal across all transition rates, diverging from the results of the computer simulations. Overall, the proposed model's ability to successfully predict pilot detection across a spectrum of transition rates marks an advancement towards developing more human-like automation.

The introduction of Trajectory Based Operations (TBO) is set to change the operation on all levels of Air Traffic Control (ATC), including aerodrome control. Here, adherence to a planned four-dimensional trajectory is important to achieve a stable operation. At the same time, this concept provides possibilities for support tools to be used in ATC, by making use of new data and information. This research presents the Take-off Timing Support Tool (TTST) for aerodrome control, and more specifically runway control, at Amsterdam Airport Schiphol. It is incorporated into the already existing Electronic Flight Strip System and features a time axis on which flight strips can be placed. By dragging departures along the time axis a planning and sequence can be constructed, while taking into account the provided solution space to prevent conflicts. An experiment conducted with the TTST showed that professional air traffic controllers used the information from the tool in their decision-making, leading to a decrease in conflict count, and it was demonstrated that a stable planning could be established. Therefore, the TTST is a suitable platform to support TBO in aerodrome control and assists air traffic controllers with time-based control. No significant difference in departure interval was present. The workload did increase moderately.

Haptic shared controllers (HSCs) are a promising solution to prevent human over-reliance on automation during tasks such as car driving. However, research has shown that if the HSC is tuned incorrectly, then there is a risk of haptic conflicts between the human and HSC. To address this challenge, this paper presents the design and implementation of a novel adaptive HSC that continuously adjusts its look-ahead time. By estimating the time shift between the reference state of the HSC and the actual state, the HSC adapts to the look-ahead time of the human it is interacting with. Results from a human-in- the-loop experiment show that the novel HSC achieves similar subjective ratings as a fixed preview HSC, as well as a significant improvement over a fixed pursuit HSC. Going from pursuit to preview, objective experiment data shows that as the adaptive HSC adjusts its look-ahead time, haptic conflicts are reduced and tracking performance is increased. The presented findings are a step forward in designing haptic support systems with high chances of user acceptance. The proposed adaptive look-ahead algorithm provides a new method for online estimation of human look-ahead time, with or without a HSC in-the-loop.

Roll reversal errors, where the pilot tries to steer the aircraft back to wings-level but unintentionally increases the bank angle instead, have contributed to several accidents. Previous studies have shown that these errors can be caused by misinterpreting the attitude indicator (AI), with the figure-ground relations cited as contributing to this misinterpretation. A modified AI was developed, which uses several static monocular depth cues (color gradient, linear perspective lines, and shadow-light relationship) to strengthen the figure-ground relationship.
The modified version of the AI was compared to a baseline AI in a two-part flight simulator experiment where pilot reaction time and error rate, severity, and duration were measured. The first part induced the leans illusion making use of physiological adaptation to roll angle, distraction, and surprise. The second part simulated the leans illusion by simply rolling the simulator to the left or right. A group of 25 experienced commercial airline pilots performed a roll-to-level task in a moving-base simulator, which also provided spatially disorienting motion cues, using both the baseline and modified versions of the AI. While the modified
display had a lower error rate in the motion-opposite scenario when using the novel method (4.91% compared to 6.07%), no significant difference was found between the error rate of the two displays. The only significant difference was found in the reaction time, where the modified AI caused an increase in reaction time. The error rates and reaction times of the first part of the experiment did not match previous research. The novel disorientation method seemed to work best in a surprise scenario. While no significant differences were found between the modified AI and the baseline AI, it is still recommended to continue testing the modified AI with a new experiment setup, especially analyzing its effect in more extreme attitudes.

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.

An important bottleneck in managing inbound traffic capacity is the limited size of the Terminal Manoeuvring Area, which should not become crowded by aircraft arriving from the Control Area. This is done by arrival metering, a process entailing sending aircraft through the Initial Approach Fix (IAF) at pre-established Expected Approach Times (EAT). Currently, area controllers are required to deliver aircraft at the IAF at their EAT ± 120 seconds. Reducing this window in the near future would allow for less buffer times between aircraft to increase capacity, potentially allowing implementation of time-based separation at the IAF as an initial step towards trajectory based operations. This research aims to produce a visual decision support tool to aid the aircraft sequencing process and achieving EAT adherence at the IAF, that is compatible with the currently in use or upcoming systems used in the Amsterdam Area Control Center. To avoid significant changes to the modus operandi of controllers, the aim is to show the user the operational constraints and focus on expediting traffic. An experiment was conducted with eight professional controllers to compare the newly developed tool with a rendition of the current interface. The results are positive, indicating less deviation from the EATs in all participants and overall lower participant workload as a result of using the proposed decision support tool.