An online pilot manual control behavior identification method, based on recursive low-order time-series model estimation, is presented and validated using experimental data. Eight participants performed compensatory tracking tasks with time-varying vehicle dynamics, where, at an unpredictable moment during a run, a sudden degradation in dynamics could occur. They were instructed to push a button when they detected a change in dynamics. Two methods to automatically detect the moment when pilot adaptation occurs from online estimated parameter traces are discussed. Results show that pilots are more accurate in detecting changes than either algorithm. But when the algorithms are correct, they are often quicker to detect pilot adaptation than pilots themselves. The presented techniques have potential but need improvements.

The identification of time-varying, adaptive behavior of a human operator in basic manual control tasks is currently still a focus area, since most methodologies only account for time-invariant system dynamics. Previous authors have proven that estimation techniques based on ARX model structures can be used to identify time-varying HO model parameters. However, ARX methods do present several problems, such as a persistent bias in the obtained estimates of the HO model poles (neuromuscular parameters) that increases due to coupled noise and system models. Therefore, in this paper a novel identification technique based on Box-Jenkins (BJ) models is proposed, to achieve a better match between the BJ estimator's inherently uncoupled system and noise models and measured HO control dynamics. The identification process was tested offline (batch-fitting) using Ordinary Least Squares and the Prediction Error Method for both ARX and BJ models, respectively, or online when Recursive Least Squares and Recursive PEM are employed. The BJ estimator has excellent potential as an identification tool due to its bias reduction capabilities, as clearly shown in batch-fitting, although the non-linear optimization processes decrease its convergence speed by 500%. An RPEM algorithm with a forgetting factor of λ = 0.99609 and a first-order remnant model incorporated in the BJ structure was tested on Monte Carlo simulation and experimental data. While the recursive BJ estimator showed the same bias-diminishing advantages also seen in batch-fitting, the non-linear RPEM estimator's results showed much slower convergence after HO behavior adaptations and frequent instabilities of the obtained parameter estimates. Hence, further research is needed for implementing a practical bias-free HO model estimator based on the BJ model structure.

Providing adequate simulator motion cues for simulated upset and stall scenarios remains challenging. This paper evaluates the potential of novel optimization-based motion cueing algorithms for upset and stall simulation. An offline analysis is performed to compare three Model Predictive Control (MPC) algorithms with varying prediction horizon lengths and prediction strategies (i.e., "Oracle", "Perfect", and "Constant") against two baseline classical washout algorithm implementations from literature. The analysis is performed for a symmetric stall scenario flown with TU Delft's Cessna Citation II laboratory aircraft. Overall, the analysis shows that the objective motion cueing quality expressed in terms of the Root Mean Square Error (RMSE) improves by 29.8% (for specific forces) and 18.7% (for rotational velocities) with the "Oracle" and "Perfect" MPC implementations compared to the reference classical washout results. For the "Constant" MPC algorithm, which in fact does not include any explicit prediction across the MPC algorithm's future prediction window, only a marginal improvement in motion quality was found. Overall, these results imply that, assuming a sufficient future reference motion prediction can be achieved, optimization-based motion cueing algorithms have the potential to provide significantly better motion cueing quality compared to classical motion cueing algorithms.

This paper presents a three-step validation approach for subjective rating predictions of driving simulator motion incongruences based on objective mismatches between reference vehicle and simulator motion. This approach relies on using high-resolution rating predictions of open-loop driving (participants being driven) for ratings of motion in closed-loop driving (participants driving themselves). A driving simulator experiment in an urban scenario is described, of which the rating data of 36 participants was recorded and analyzed. In the experiment's first phase, participants actively drove themselves (i.e., closed-loop). By recording the drives of the participants and playing these back to themselves (open-loop) in the second phase, participants experienced the same motion in both phases. Participants rated the motion after each maneuver and at the end of each drive. In the third phase they again drove open-loop, but rated the motion continuously, only possible in open-loop driving. Results show that a rating model, acquired through a different experiment, can well predict the measured continuous ratings. Second, the maximum of the measured continuous ratings correlates to both the maneuver-based (ρ =0.94) and overall (ρ =0.69) ratings, allowing for predictions of both rating types based on the continuous rating model. Third, using Bayesian statistics it is then shown that both the maneuver-based and overall ratings between the closed-loop and open-loop drives are equivalent. This allows for predictions of maneuver-based and overall ratings using the high-resolution continuous rating models. These predictions can be used as an accurate trade-off method of motion cueing settings of future closed-loop driving simulator experiments.

While human control behavior is well-understood in continuous control tasks, little is still known about how human operators detect sudden changes in the controlled element dynamics. This paper focuses on modeling this detection phase for pursuit tracking tasks. Potential triggers for the human operator to detect changes in the controlled element dynamics were investigated via a time-varying computer simulation. Based on the results, hypotheses were generated and later tested in a single-axis pursuit tracking experiment with fifteen participants. Transitions from approximate single to approximate double integrator dynamics and vice versa were investigated, for which participants indicated if they detected the transition by pressing a button. Using the button push data, a model for each transition was developed and validated. The models work under the assumption that human operators use a threshold, a multiple of the steady-state standard deviation, on certain signals to detect transitions. The models developed for the transition from single to double integrator dynamics and vice versa are proposed to trigger on the tracking error and system output acceleration, respectively. They have an accuracy of 88.9% and 99.4%, respectively. However, a consistent underestimation of the detection lag remains a limitation of both models. Nonetheless, this research helped confirm the tracking error can be used in a model for the transition from single to double integrator dynamics, proposed a model for the opposite transition, and identified that the relationship between control inputs and the system's response as a crucial factor for the detection.

We investigated the effect of personality traits and flight experience on pilot cognitive and affective responses across seven startling and surprising scenarios performed in motion-based simulators. A dataset of 89 airline pilots from four studies was used. The personality traits measured were trait anxiety, decision-related action orientation (AOD), and failure-related action orientation (AOF). Pilot self-reported responses in scenarios were standardized by obtaining z scores of startle, surprise, stress, and mental workload. Only trait anxiety was found to be significantly positively correlated with stress. No significant effects of AOD, AOF, or flight hours were found on pilots’ responses. The results indicate trait anxiety may affect pilots’ responses to stressful scenarios, even though pilots are selected based on low trait anxiety.

This article discusses a long short-term memory (LSTM) recurrent neural network that uses raw time-domain data obtained in compensatory tracking tasks as input features for classifying (the adaptation of) human manual control with single- and double-integrator controlled element dynamics. Data from two different experiments were used to train and validate the LSTM classifier, including investigating effects of several key data preprocessing settings. The model correctly classifies human control behavior (cross-experiment validation accuracy 96%) using short 1.6-s data windows. To achieve this accuracy, it is found crucial to scale/standardize the input feature data and use a combination of input signals that includes the tracking error and human control output. A possible online application of the classifier was tested on data from a third experiment with time-varying and slightly different controlled element dynamics. The results show that the LSTM classification is still successful, which makes it a promising online technique to rapidly detect adaptations in human control behavior.

Previous research indicated a need to improve pilot training with regard to understanding of autopilot logic and behavior, especially in non-routine situations. Therefore, we tested the effect of problem-based exploratory training on pilots’ understanding of autopilot functions. Using a moving-base flight simulator, general aviation pilots (n = 45) were trained to diagnose failures either without foreknowledge and guidance (exploratory group), without foreknowledge but with some guidance (exploratory-guidance group) or with foreknowledge and full guidance (control group). They subsequently performed six test scenarios in which their understanding of the effects of failures was tested by requiring them to deduce the failures and select autopilot modes that were still functioning. Those who received exploratory training with guidance were significantly more likely than the other groups to diagnose failures correctly. The exploratory training group also selected the most appropriate functioning autopilot modes significantly faster than the control group. The results suggest that exploratory training with an appropriate level of guidance is useful for gaining a practical understanding of autopilot logic and behavior. Exploratory training may help to improve transfer of training to operational practice, and prevent automation surprises and accidents.

In moving-base driving simulators, the sensation of the inertial car motion provided by the motion system is controlled by the motion cueing algorithm (MCA). Due to the difficulty of reproducing the inertial motion in urban simulations, accurate prediction tools for subjective evaluation of the simulator's inertial motion are required. In this article, an open-loop driving experiment in an urban scenario is discussed, in which 60 participants evaluated the motion cueing through an overall rating and a continuous rating method. Three MCAs were tested that represent different levels of motion cueing quality. It is investigated under which conditions the continuous rating method provides reliable data in urban scenarios through the estimation of Cronbach's alpha and McDonald's omega. Results show that the better the motion cueing is rated, the lower the reliability of that rating data is, and the less the continuous rating and overall rating correlate. This suggests that subjective ratings for motion quality are dominated by (moments of) incongruent motion, while congruent motion is less important. Furthermore, through a forward regression approach, it is shown that participants' rating behavior can be described by a first-order low-pass filtered response to the lateral specific force mismatch (66.0%), as well as a similar response to the longitudinal specific force mismatch (34.0%). By this better understanding of the acquired ratings in urban driving simulations, including their reliability and predictability, incongruences can be more accurately targeted and reduced.

Understanding human perception of haptic feedback is critical when designing and regulating these interfaces. In recent years, experiments have been conducted to determine the just-noticeable difference (JND) in mass-spring-damper dynamics, using a hydraulic admittance display in the form of a side-stick. These experiments have resulted in a model of JNDs when interacting with linear second-order dynamics. In real-world applications, however, control force dynamics also commonly include nonlinearities, such as friction. This research extends the current understanding of JNDs in linear systems by including the nonlinear case, where friction is also present. Experiments were conducted to determine JNDs in friction when combined with second-order system dynamics. Results indicate that friction JND can be independent of linear system dynamics as long as its value compared to the linear system's impedance is sufficiently large. As a consequence, friction JND follows Weber's law, also when it is combined with mass-spring-damper dynamics, unless the level of friction approaches the detection threshold, which in turn can be influenced by the linear system dynamics. Based on the findings presented, it is possible to conduct targeted experiments to confirm and add to these initial results.

Automation errors may result in human performance issues that are often difficult to grasp. Skraaning and Jamieson (2023) proposed a taxonomy for classifying automation errors into categories based on the visible symptoms of design problems, so as to benefit the design of training scenarios. In this paper, we propose a complementary classification that is based on the mechanisms of human-automation interaction guided by Rasmussen’s Skill, Rule and Knowledge (SRK) taxonomy. We identified four main failure classes and expect that this classification can support automation designers.

The collective goal of the driving simulation community should be to share ideas to improve the motion cueing across driving simulators worldwide. Due to the active research and intensive usage of driving simulators over the last decades, knowledge in the field of motion cueing has been gained from experience gathered by the community, or practical experience by performing dedicated experiments. This paper discusses several points of ‘common knowledge’ in designing and evaluating motion cueing, along with their value for driving simulation. The goal of the discussion in this paper is to compare these points of common knowledge to the experiences and ideas gathered at BMW’s driving simulation center in Munich, which was opened in 2021 and hosts a fleet of fourteen driving simulators. Furthermore, we aim to bring across points of interest and outlines for future research that should be of interest to the driving simulation community. With the common goal of improving motion cueing, this contribution thus aims to improve and extend the discussion between those working and researching in the driving simulator industry.

Preventing Loss of Control In-flight (LOC-I) in commercial and general aviation is an active research area with numerous proposed solutions. One of these solutions aims to prevent lateral LOC-I, a special type of LOC-I, by presenting a roll-performance based minimum lateral control speed to the pilot in roll-limited situations, such as single-engine failure scenarios in multi-engine aircraft. This minimum lateral control speed is predicted by a system, named the Vc Prediction System (VPS), which continually predicts the minimum lateral control speed Vc at which an aircraft can still obtain a certain roll angle within a certain amount of time. It consists of three components; a linear model, a parameter estimation method and a Vc prediction model. These VPS components were designed for a simulation model of the Piper Seneca. This study analyzes the sensitivity of the VPS design to a change in aircraft dynamics and simulation model complexity by redesigning this system for a high-fidelity simulation model of the Fokker 50. The results show that both aircraft favor a small linear model and the Modified Kalman Method for parameter estimation. The original Vc prediction model however gives higher Vc prediction errors for the Fokker 50 than for the Piper Seneca. By simplifying the original Vc prediction model a stable, smooth and relatively accurate Vc prediction for the Fokker 50 can be obtained.

In academic air traffic control research, traffic scenarios are often repeated to increase the sample size and enable paired-sample comparisons, e.g., between different display variants. This comes with the risk that participants recognize scenarios and consequently recall the desired response. In this paper we provide an overview of mitigation techniques found in literature and conclude that rotating scenario geometries is most frequently used. The potential impact of these transformations on participant behavior, as described in this paper, is however not sufficiently addressed in most studies. As an example we, therefore, analyze previously collected eye tracking data from ten professional air traffic controllers, each presented with three repetitions in various rotations of several distinct scenarios. Results imply that researchers wishing to repeat scenarios should more carefully consider whether mitigation techniques might have an impact on their results.

Due to the non-deterministic nature of longitudinal human driver behaviour, motion cueing algorithms currently cannot fully utilize the workspace of driving simulators. This paper explores the possibility of using various predictor variables to predict longitudinal driving behaviour. Through the development of a logistic regression model, it is shown that a combination of the current vehicle velocity, the speed limit eight seconds ahead and the accelerator pedal deflection yields the most accurate estimate of the probabilities that drivers will accelerate or decelerate. Based on these probabilities, a driving simulator was linearly pre-positioned in combination with a classical washout algorithm. The perceived motion incongruence was subjectively evaluated by the drivers (N = 34), testing: (i) no pre-positioning, (ii) pre-positioning, and (iii) pre-positioning with an increased longitudinal classical washout gain enabled by the pre-positioning. Results show that the pre-positioning improves the margins with respect to the longitudinal workspace limits (better workspace management), without affecting the motion incongruence ratings. When using the increased margins to increase the longitudinal gain, however, no significant reduction in motion incongruence ratings was observed. This is likely due to the small motion space of the hexapod motion system used in the current study. However, this paper shows that longitudinal driving behaviour can be accurately predicted and can enable improved workspace utilization for driving simulators.

An improved understanding of pilot’s control behavior adaptations in response to sudden changes in the vehicle dynamics is essential for realizing adaptive support systems that remain effective when task characteristics suddenly change. In this paper, we replicate, extend, and validate the ‘adaptive pilot model’ proposed by Hess to verify its effectiveness for predicting human adaptive behavior in pursuit tracking tasks. The model relies on a Triggering function, that compares the current tracking performance to a stored nominal (pre-transition) state, and an Adaptation mechanism which determines new adapted human operator gain settings proportional to the magnitude of the off-nominal error occurrences. For model validation data from a previous experiment were used, where ten participants performed a pursuit tracking task with transitions in controlled element dynamics from a single to a double integrator, and vice versa. Overall, with an added human operator delay and participant-specific inner- and outer-loop gain adjustments, the model was found to accurately describe the measured steady-state tracking behavior for the participants in our data set. The results for the time-varying single integrator to double integrator transitions showed that the model can capture the transient control behavior of participants. However, the adaptive logic could only be tuned to activate for participants that had a pre-transition crossover frequency above 0.9 rad/s. Furthermore, the model was not able to capture the change in control behavior for transitions from a double to a single integrator. Here, as no distinct degradation in tracking performance occurs for such a transition to a more easily controlled system, the model's proposed Triggering logic will not activate. Further investigation and more experiment data are required for improving the applicability of the model's adaptive logic and to enable more accurate prediction of adaptive human control behavior.

Haptic cues on the side stick are a promising method to reduce loss of control in-flight incidents. They can be intuitively interpreted and provide immediate support, leading to a shared control system. However, haptic interfaces are limited in providing information, and the reason for cues may not always be clear to pilots. This study presents the results of the conceptual development of visual display symbology that supports haptic feedback on the side stick in communicating flight envelope boundaries to pilots. Novel indications for the limits of airspeed, load factor, angle of attack, and angle of bank, which for the first time simultaneously indicate magnitude and direction of the haptic cues, were integrated in an Airbus primary flight display. The symbology was tested in a pilot-in-the-loop experiment with professional Airbus pilots (N=16) flying several approaches in alternate law with haptic feedback. Objective results do not show clear improvements, although the time spent outside the flight envelope is slightly reduced. Subjective results indicate a preference, however, for the new display and an increased understanding of the haptic feedback. Further research is recommended to improve the interface design, remove unused indications, and test a bank scenario using current operational bank limits.

This paper describes how the kinematic configuration of a driving simulator's motion system affects the rendered inertial motion. The specific force and rotational rate equations between the point where the motion is applied (Motion Reference Point (MRP)), and the point in which the driver perceives the motion (Cueing Reference Point (CRP)), are derived for three kinematic configurations: (i) a hexapod, (ii) a hexapod with an xy-drive and a yaw-drive below, and (iii) the same system as (ii), but with the yaw-drive on top. The rotational rate equations show that having a yaw-drive on top greatly complicates the motion control. Furthermore, simulation results show that, regardless of the yaw-drive location, the difference between MRP and CRP becomes noticeable for large yaw-drive excitations. For such driving simulators, the positional offset between MRP and CRP can therefore not be ignored, complicating the motion control.

In driving simulation, the choice of a simulator, motion cueing algorithm, and associated set of tuning parameters for an experiment is typically made with an exclusive focus on the quality of the motion. In practice, many other metrics could affect this choice as well, such as tuning complexity, algorithm stability, or the financial costs of the simulation. Arguably, the complete motion cueing algorithm quality is thus more than the quality of the motion alone. This paper presents results of a survey which attempted to identify the most important metrics from the perspective of the main experiment stakeholders. Four stakeholder groups in typical driving simulator experi- ments are defined: The experimenters, motion cueing engineers, operators, and participants. All groups received the same survey, asking them to indicate how important various metrics are for them. Results show that, next to the quality of the motion, experimenters and participants are generally interested in reducing simulator sickness. The motion cueing engineers rank tuning effort and tuning complexity as most important metrics. Operators prefer an easy to use and overall stable motion cueing. A typical BMW experiment is discussed as example, which shows that the choice for a simulator and motion cueing algorithm can indeed differ when including these metrics in a trade-off, compared to when only motion quality is considered. The presented methods allow for a better, multi- faceted selection of the simulator, motion cueing algorithm, and associated tuning parameters, improving future driving simulation experiments.

Accurate process control through automation is the key to achieving efficient and stable operation of a blast furnace. In this study, we developed an automatic control system of hot metal temperature (HMT). To cope with the slow and complex process dynamics of the blast furnace, we constructed a control algorithm that predicts eight-hour-ahead HMT using a two-dimensional (2D) transient model and calculates optimal target pulverized coal ratio (PCR) and pulverized coal flow rate by non-linear model predictive control (NMPC). An evaluation in a real plant showed that the developed control system suppressed the effects of disturbances, such as changes in the coke ratio and blast volume, on the HMT. The root mean square (RMS) of the control deviation of HMT was successfully reduced by 1.6 °C compared to the conventional manual operation.