Driving simulators are essential tools to guide automotive research and development. Their motion system requires a motion cueing algorithm (MCA) to keep the simulator motion platform within its physical boundaries, while simultaneously aiming to recreate the sensation of real vehicle motion. While traditional, filter-based approaches are still predominant, optimization-based MCAs have been at the center of MCA research for over a decade due to their ability to systematically improve motion cueing quality through explicit cost function design and constraints handling. However, despite their demonstrated advantages, these optimization-based methods have not yet achieved widespread adoption in driving simulation. This paper therefore provides a comprehensive review of optimization-based MCAs for driving simulation, categorizing and comparing algorithms, describing their key developments and core characteristics. The current limited real-time capability, lack of accurate evaluation methods, challenges in cost function design and its tuning, and the current lack of accurate future reference predictions are identified as key barriers to the practical deployment and widespread use of optimization-based MCAs. These theoretical and practical challenges are further reviewed, providing guidelines to advance the theory and application of optimization-based MCAs. Central in these advancements are a better understanding of which motions constitute a realistic motion experience, a framework allowing to compare the achieved motion fidelity of MCAs across papers, the design of the cost function focusing on human motion perception, and techniques for easing up the tuning process to swiftly reach high quality tunings for different simulators, scenarios, and use cases. We identify the need for improving the real-time capability, and providing high quality motion reference predictions using learning-based approaches on diverse datasets, along with techniques to handle existing uncertainties. Following these guidelines, new foundations for optimization-based algorithms in driving simulation can be achieved, which will significantly impact the research and development of automotive systems.

A pursuit-tracking manual control model is introduced that includes an observer-like internal model to predict human detection of a change in controlled element dynamics. The internal model’s innovation signal, the difference between the observed and expected system response, is studied for its capacity to drive the detection of a change. The model’s performance is tested for different crossover frequencies, remnant power ratios, observer gains, and detection threshold settings, through Monte Carlo analysis of simulated pursuit-tracking tasks where the controlled element transitions from single to double integrator dynamics. The model shows highly accurate detection performance for a wide range in the observer gain, with a true positive rate of approximately 1 and a false positive rate of approximately 0.02. The high true and low false positive rates, combined with average detection times that match experimental human-in-the-loop data, show the observer model’s potential for accurately predicting human detection of a change in controlled element dynamics.

Haptic shared control systems that support drivers by means of added torques on the steering wheel are often tuned heuristically. To allow for more systematic design, this paper focuses on the Four Design Choices Architecture (FDCA) and systematically analyzes its tuning with an offline simulation model for the driver’s control behavior and neuromuscular system. These analyses indicated that within the FDCA architecture the Level of Haptic Support (LoHS), which is a feedforward channel supporting negotiation of upcoming curves, is a main contributor to joint system performance. In a driving simulator experiment, the adaptation to and acceptance of different LoHS levels was investigated. Driver acceptance was found to increase with increasing LoHS values up to 1. Objective metrics, including torque conflict (70% reduction), steering effort (81% reduction), steering wheel reversal rate, and lateral deviation all improved, indicating that with the FDCA a high LoHS is both acceptable and, in fact, preferred.

InceptionTime neural network models were trained to detect distractions in manual control tasks with pursuit and preview displays. Training and test data were collected in an experiment where ten participants were deliberately distracted from the primary control task using the Surrogate Reference Task. Overall, distractions are easier to detect in pursuit tasks, with test accuracies of around 80% and 60% for pursuit and preview data, respectively. With preview, human controllers see the future target trajectory, which enables them to mitigate distraction effects. Unexpectedly, data with longer distractions from ‘hard’ secondary tasks are more difficult to classify than ‘easy’ distractions; an effect attributed to differences in human behavior between the training and test data collection conditions. These results show clear opportunities for neural network models to detect distractions, in real-time, for increasing safety of human-operated vehicles.

This paper applies model-based biodynamic feedthrough (BDFT) cancellation to a touchscreen dragging task during realistic vertical (vertical) and lateral (horizontal) aircraft turbulence, to mitigate erroneous turbulence-induced inputs. One-size-fits-all (OSFA) BDFT models were used to model the influence of turbulence accelerations on finger position, achieving average quality-of-fits of 61% and 69% in the vertical and horizontal screen directions, respectively. On average, 27% of the touch input error variance was mitigated these OSFA models, with individualized models providing only a marginal improvement (+4%). The application of OSFA models identified from a condition with equally-scaled vertical and horizontal motion (adjusted intensity) to the realistic turbulence condition did not significantly affect cancellation performance, indicating that BDFT models may not need to be adaptive to varying motion intensity. However, consistent with earlier work, BDFT dynamics were found to vary between vertical and horizontal finger movements, with BDFT dynamics exhibiting lower stiffness and a higher static gain for vertical BDFT. On average, the linear BDFT-related component of touch input errors contributed 41% of the overall error variance, indicating that current linear BDFT model may need to be extended to include nonlinear effects, such as varying finger friction.

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 ($\rho =0.94$) and overall ($\rho =0.69$) 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.

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 thebetterthe motion cueing is rated, thelowerthe 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.

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.

Biodynamic feedthrough (BDFT) is a key issue for touchscreen operations on the future flight deck, as cockpit accelerations due to turbulence leave pilots vulnerable to erroneous touches that disrupt task performance. This research focuses on the implementation of a software-based cancellation approach to mitigate the adverse effects of BDFT in touchscreen dragging tasks. A flight-simulator experiment with 18 participants was performed to estimate models of BDFT dynamics for horizontal and vertical touch-inputs on a primary flight display. The averaged BDFT models were used to cancel BDFT in the same continuous dragging task used for model identification and a discrete point-to-point dragging task. While for the continuous task the cancellation enabled 63% mitigation in BDFT, the same cancellation was ineffective for the discrete task, due to reduced BDFT susceptibility. Overall, the results show that while model-based BDFT cancellation can be highly effective, a key technical challenge will be ensuring it is sufficiently task-adaptive.

The 1960s crossover model is widely applied to quantitatively predict a human controller's (HC's) manual control behavior. Unfortunately, the theory captures only compensatory tracking behavior and, as such, a limited range of real-world manual control tasks. This article finalizes recent advances in manual control theory toward more general pursuit and preview tracking tasks. It is quantified how HCs adapt their control behavior to a final crucial task variable: the target trajectory bandwidth. Beneficial adaptation strategies are first explored offline with computer simulations, using an extended crossover model theory for pursuit and preview tracking. The predictions are then verified with data from a human-in-the-loop experiment, in which participants tracked a target trajectory with bandwidths of 1.5, 2.5, and 4 rad/s, using compensatory, as well as pursuit and preview displays. In stark contrast to the crossover regression found in compensatory tasks, humans attenuate only their feedforward response when tracking higher-bandwidth trajectories in pursuit tasks, while their behavior is generally invariant in preview tasks. A full quantitative theory is now available to predict HC manual control behavior in tracking tasks, which includes HC adaptation to all key task variables.

Novel driver support systems potentially enhance road safety by cooperating with the human driver. To optimize the design of emerging steering support systems, a profound understanding of driver steering behavior is required. This article proposes a new theory of driver steering, which unifies visual perception and control models. The theory is derived directly from measured steering data, without any a priori assumptions on driver inputs or control dynamics. Results of a human-in-the-loop simulator experiment are presented, in which drivers tracked the centerline of straight and winding roads. Multiloop frequency response function (FRF) estimates reveal how drivers use visual preview, lateral position feedback, and heading feedback for control. Classical control theory is used to model all three FRF estimates. The model has physically interpretable parameters, which indicate that drivers minimize the bearing angle to an “aim point” (located 0.25-0.75 s ahead) through simple compensatory control, both on straight and winding roads. The resulting unifying perception and control theory provides a new tool for rationalizing driver steering behavior, and for optimizing modern steering support systems.

Did you know that most drivers swing left before taking a right curve? In fact, this is a given for all race car drivers and a rule for efficient curve negotiation. This distinct way of approaching a curve is called prepositioning. In a recent study it is found that incorporating knowledge of this prepositioning phase is crucial for the reliability and acceptance of some trajectory-guiding advanced-driver-assistance-systems. Unfortunately, our understanding of prepositioning behaviour is still limited, with most driver models unable to account for this phenomenon. In an attempt to improve our understanding, the effects of changing velocity and road radius on prepositioning behaviour are studied experimentally in a fixed-base driving simulator. Twenty-four participants drove four conditions comprising two different fixed-speed velocities (50 and 80 km/h) and two different curve radii (204 and 350 m). The results show that the drivers' maximum prepositioning position significantly increases with increasing velocity and significantly decreases with increasing radius. With 88% of the runs exhibiting a significant displacement, i.e. larger than 0.05 m relative to a constant road bias. The findings suggest that drivers adjust their prepositioning behaviour to the Time-to-Line-Crossing (TLC) of the road environment, in an attempt to maximise TLC. Incorporating these findings in future driver modelling will bridge the gap between straight road and in-curve driving behaviour, thereby bolstering the descriptive capacity of these models.

When taking a curve, drivers follow their own unique trajectory. Most driver style classifiers in literature are based on inertial inputs, denoting whether a given driver is aggressive or calm. However, this does not give any indication of a drivers trajectory style, i.e. whether a driver is curve cutting. To fill this void, this paper introduces a novel rule based classifier that categorises seven different trajectory styles. The classifier is applied to data from a fixed-base driving simulator study in which 45 subjects drove on three roads, comprising three different velocities: 25, 50 and 80 km/h, with three corresponding radii: 20, 80 and 204 m. The results show that some classes are more prevalent than others, with biased outer curve negotiation performed by a majority of the subjects and with no drivers classified as centerline drivers. The proposed trajectory classifier is shown to exhibit high levels of consistency, with 93% of drivers exhibiting consistent trajectory classes for at least 66% of the right curves driven and 84% exhibits consistent trajectory classes for atleast 66% of the left curves driven. Where this consistency indicates a potential for generalising the classification results to other curves. Additionally, this classifier can be used to adapt trajectory-driven advanced driver assistance systems, thereby serving as an alternative to driver modelling.

The human controller (HC) in manual control of a dynamical system often follows a visible and predictable reference path (target). The HC can adopt a control strategy combining closed-loop feedback and an open-loop feedforward response. The effects of the target signal waveform shape and the system dynamics on the human feedforward dynamics are still largely unknown, even for common, stable, vehicle-like dynamics. This paper studies the feedforward dynamics through computer model simulations and compares these to system identification results from human-in-the-loop experimental data. Two target waveform shapes are considered, constant velocity ramp segments and constant acceleration parabola segments. Furthermore, three representative vehicle-like system dynamics are considered: 1) a single integrator (SI); 2) a second-order system; and 3) a double integrator. The analyses show that the HC utilizes a combined feedforward/feedback control strategy for all dynamics with the parabola target, and for the SI and second-order system with the ramp target. The feedforward model parameters are, however, very different between the two target waveform shapes, illustrating the adaptability of the HC to task variables. Moreover, strong evidence of anticipatory control behavior in the HC is found for the parabola target signal. The HC anticipates the future course of the parabola target signal given extensive practice, reflected by negative feedforward time delay estimates.

Vehicle control tasks require simultaneous control of multiple degrees-of-freedom. Most multi-axis human-control modeling is limited to the modeling of multiple fully independent single axes. This paper contributes to the understanding of multi-axis control behavior and draws a more realistic and complete picture of dual-axis manual control. A human-in-the-loop experiment was performed to study four distinctive phenomena that can occur in multi-axis control: performance degradation, axis asymmetry, crossfeed, and intermittency. In a simulator, three conditions were tested in the presence and absence of physical motion: the full dual-axis control task, single-axis roll task, and single-axis pitch task. Controlled element dynamics, stick dynamics, and forcing functions were equal in all cases. Results show that performance is worse in dual-axis tasks. Performance in roll axis is consistently worse than pitch, thereby proving axis asymmetry. Physical motion improves the performance and stability of the system. The application of independent forcing function signals in both controlled axes resulted in the detection of crossfeed in dual-axis tasks from spectral analysis. Using a novel extended Fourier coefficient method, the identified crossfeed dynamics can explain up to 20% of the measured control inputs and improves modeling accuracy by up to 5%. Dual-axis control behavior is less accurately modeled with linear time-invariant models and is more intermittent.

Novel (semi-)automated systems are rapidly being introduced into modern road vehicles, but anticipating possibly critical human-machine interaction issues is difficult, because the human driver's behavior is as of yet still poorly understood. This paper aims to improve our understanding and models of driver steering behavior on winding roads, using Frequency-Response Function (FRF) measurements of drivers' feedforward, heading feedback, and lateral position feedback dynamics. The steering behavior data were collected in a human-in-the-loop simulator experiment, in which drivers followed the road centerline at constant forward velocity, while being perturbed laterally by wind-gust disturbances. All three measured FRFs can be captured with a multiloop, single preview-point driver model, which has only five parameters. These parameters provide unmatched understanding of - otherwise lumped - driver internal steering processes, quantifying how and what portion of the previewed centerline trajectory is used for control, and how lateral position and heading feedback are weighed. The gained insights may help to reduce driver-automation interaction issues in modern road vehicles, to quantify between-driver steering variations, adaptation and learning, and to design human-like and individualized automatic and shared steering controllers.

In manual tracking tasks with preview of the target trajectory, humans have been modeled as dual-mode "near" and "far" viewpoint controllers. This paper investigates the physical basis of these two control mechanisms, and studies whether estimated viewpoint positions represent those parts of the previewed trajectory which humans use for control. A combination of human gaze and control data is obtained, through an experiment which compared tracking with full preview (1.5 s), occluded preview, and no preview. System identification is applied to estimate the two look-ahead time parameters of a two-viewpoint preview model. Results show that humans focus their gaze often around the model's near-viewpoint position, and seldom at the far viewpoint. Gaze measurements may augment control data for the online identification of preview control behavior, to improve personalized monitoring or shared-control systems in vehicles.

When drivers have opposing intentions to a haptic shared controller which, like the driver, can continuously control the vehicle through torques on the steering wheel, the driver has to fight against the controller torque to reach their goal. This phenomenon is called haptic shared control (steering) conflicts and are a reason for drivers to reject such automation. This study is the first to realise an implementation of the novel "Four-Design-Choice-Architecture" design philosophy for shared control, hypothesized to reduce conflicts through its inherent control structure. The implemented haptic shared controller decouples reference trajectory from independent feedback and feedforward haptic control. The implemented Four-Design-Choice haptic shared controller is compared to the baseline (predecessor) Meshed haptic shared controller through a simulator experiment. The results show that the new shared controller significantly reduces occurrence of conflicts by a factor 2.3 and significantly reduces driver torque by a factor of 3.2. Analysis shows that the novel feed-forward haptic torque and a reference trajectory supporting the drivers future (curve-entry) intentions are the dominant players in conflict reduction. The findings show that the Four-Design-Choice-Architecture is proven very effective and has large potential to further reduce conflicts with different design settings.

Traditional Frequency Response Function (FRF) estimation techniques used for analysis of Human Controller (HC) dynamics in tracking tasks assume HC dynamics to be linear, but generally do not quantify or compensate for the effects of human nonlinearities. The robust and fast Best Linear Approximation (BLA) techniques for estimating an FRF do provide such quantification of nonlinear distortions caused by Period-In-Same-Period-Out (PISPO) nonlinearities and can reduce the effect of PISPO nonlinear operations on the FRF estimate. This paper investigates the application of these BLA techniques to both measured and simulated HC data. For the simulated data, a linear HC model was deliberately extended with a symmetric PISPO deadzone nonlinear operator and a realistic level of HC "remnant" noise. Overall, both the measured and the simulated data indicate that due to the high levels of remnant noise inherent to HC data, no consistent estimate of PISPO nonlinear contributions could be made. This also means that the improvement of using BLA techniques and averaging over multiple forcing function realizations does not result in a substantial improvement over the current practice of estimating HC FRFs from repeated measurements of a single forcing function.

This paper investigates the effects of simulator motion on driver steering performance, and how this depends on the available visual information and external disturbances such as wind gusts. A human-in-the-loop driving experiment was performed in which twelve participants steered a fixedvelocity car to follow a winding road (target tracking, TT) while suppressing side-wind gusts (disturbance-rejection, DR). Driver performance with and without motion feedback is compared in six tasks: "regular" lane-keeping with optic flow, centerline tracking with optic flow, and centerline tracking without optic flow, all with both 5 and 100 m of preview. Performance is calculated in the frequency domain to separate TT and DR contributions. The results show that motion feedback always yields improved DR performance, but the actual improvement depends strongly on the available simulator visuals. TT performance is generally unaffected by motion feedback, except when preview is limited. We conclude that simulator motion is required to evoke realistic driver performance in tasks where substantial external disturbances are present, but not in disturbance-free tasks where a winding road is being followed.

Realistic manual control tasks typically involve predictable target signals and random disturbances. The human controller (HC) is hypothesized to use a feedforward control strategy for target-following, in addition to feedback control for disturbance-rejection. Little is known about human feedforward control, partly because common system identification methods have difficulty in identifying whether, and (if so) how, the HC applies a feedforward strategy. In this paper, an identification procedure is presented that aims at an objective model selection for identifying the human feedforward response, using linear time-invariant autoregressive with exogenous input models. A new model selection criterion is proposed to decide on the model order (number of parameters) and the presence of feedforward in addition to feedback. For a range of typical control tasks, it is shown by means of Monte Carlo computer simulations that the classical Bayesian information criterion (BIC) leads to selecting models that contain a feedforward path from data generated by a pure feedback model: "false-positive" feedforward detection. To eliminate these false-positives, the modified BIC includes an additional penalty on model complexity. The appropriate weighting is found through computer simulations with a hypothesized HC model prior to performing a tracking experiment. Experimental human-in-the-loop data will be considered in future work. With appropriate weighting, the method correctly identifies the HC dynamics in a wide range of control tasks, without false-positive results.

This paper investigates how humans use a previewed target trajectory for control in tracking tasks with various controlled element dynamics. The human's hypothesized "near" and "far" control mechanisms are first analyzed offline in simulations with a quasi-linear model. Second, human control behavior is quantified by fitting the same model to measurements from a human-in-the-loop experiment, where subjects tracked identical target trajectories with a pursuit and a preview display, each with gain, single-, and double-integrator controlled element dynamics. Results show that target-tracking performance improves with preview, primarily due to the far-viewpoint response, which allows humans to cancel their own and the controlled element's lags, without additional control activity. The near-viewpoint response yields better target tracking at higher frequencies, but requires substantially more control activity. The control-theoretic approach adopted in this paper provides unique quantitative insights into human use of preview, which can help to explain human behavior observed in other preview control tasks, like driving.

Motion cueing algorithms are used in motion simulation to map the inertial vehicle motion onto the limited simulator motion space. This mapping causes mismatches between the unrestricted visual motion and the constrained inertial motion, which results in perceived motion incongruence (PMI). It is still largely unknown what exactly causes visual and inertial motion in a simulator to be perceived as incongruent. Current methods for measuring motion incongruence during motion simulation result in time-invariant measures of the overall incongruence, which makes it difficult to determine the relevance of the individual and short-duration mismatches between visual and inertial motion cues. In this paper, a novel method is presented to subjectively measure the time-varying PMI continuously throughout a simulation. The method is analyzed for reliability and validity of its measurements, as well as for its applicability in relating physical short-duration cueing errors to PMI. The analysis shows that the method is reliable and that the results can be used to obtain a deeper insight into the formation of motion incongruence during driving simulation.

Due to linear perspective, the visual stimulus provided by a previewed reference trajectory reduces with increasing distance ahead. This paper investigates the effects of linear perspective on human use of preview in manual control tasks. Results of a human-in-the-loop tracking experiment are presented, where the linear perspective's horizontal and vertical deformations along the previewed trajectory were applied separately and combined, or were absent (plan-view task). Measurements are analyzed with both nonparametric and parametric system identification techniques, in combination with a quasi-linear human controller model for plan-view preview tracking tasks. Results show that reduced visual stimuli in perspective tasks evoke less aggressive control behavior, but that the human's underlying control mechanisms are still accurately captured by the model. We conclude that human controllers use preview information similar in plan-view and perspective tasks.

In manual control tasks, preview of the target trajectory ahead is often limited by poor lighting, objects, or display edges. This paper investigates the effects of limited preview, or preview time, in manual tracking tasks with single- and double-integrator controlled element dynamics. A quasi-linear human controller model is used to predict the human behavior adaptations offline, by finding the model parameters that yield optimal performance at each preview time. These predictions are then verified by fitting the same model to measurements from a human-in-the-loop experiment, where subjects performed a tracking task with eight different preview time settings between 0 and 2 s. Results show that the tracking performance improves and the model's “look-ahead” time parameters increase with increasing preview time. Beyond a certain preview time, approximately 0.6 s and 1.15 s in single- and double-integrator tasks, respectively, additional preview evokes no further adaptations. The offline model predictions closely match the experimental results, which thereby promises to facilitate similar quantitative insights in other tasks with restricted preview.

Manual control cybernetics aims to understand and describe how humans control vehicles and devices using mathematical models of human control dynamics. This “cybernetic approach” enables objective and quantitative comparisons of human behavior, and allows a systematic optimization of human control interfaces and training associated with manual control. Current cybernetics theory is primarily based on technology and analysis methods formalized in the 1960s and has shown to be limited in its capability to capture the full breadth of human cognition and control. This paper reviews the current state-of-the-art in our knowledge of human manual control, points out the main fundamental limitations in cybernetics, and proposes a possible roadmap to advance the theory and its applications. Central in this roadmap will be a shift from the current linear time-invariant modeling approach that is only truly valid for human behavior under tightly controlled and stationary conditions, to methods that facilitate the analysis of adaptive, and possibly time-varying, human behavior in realistic control tasks. Examples of key current developments in the field of cybernetics-human use of preview, predictable discrete maneuvering, skill acquisition and training, time-varying human modeling, and neuromuscular system modeling-that contribute to this shift are presented in this paper. The new foundations for cybernetics that will emerge from these efforts will impact all domains that involve humans in manual and semiautomatic control.

Real-life tracking tasks often show preview information to the human controller about the future track to follow. The effect of preview on manual control behavior is still relatively unknown. This paper proposes a generic operator model for preview tracking, empirically derived from experimental measurements. Conditions included pursuit tracking, i.e., without preview information, and tracking with 1 s of preview. Controlled element dynamics varied between gain, single integrator, and double integrator. The model is derived in the frequency domain, after application of a black-box system identification method based on Fourier coefficients. Parameter estimates are obtained to assess the validity of the model in both the time domain and frequency domain. Measured behavior in all evaluated conditions can be captured with the commonly used quasi-linear operator model for compensatory tracking, extended with two viewpoints of the previewed target. The derived model provides new insights into how human operators use preview information in tracking tasks.

This paper describes a new approach for analyzing training effectiveness in transfer-of-training experiments, by considering the between-subject variability of post-transfer changes in task performance and control activity of individual trained pilots. First, exponential learning curve models were fit on experimental data of individual pilots. Second, curve parameters were used to analyze the immediate changes in task performance and control gain following transfer, and the correlation between immediate changes in task performance and continued learning rate after transfer. Data from two experiments with different experimental designs were compared using the new approach. The method revealed similar post-transfer effects in the immediate changes in task performance and control gain following transfer between the two experiments when pilots trained without motion. However, differences in post-transfer effects were found when comparing the correlations between the immediate change in task performance and learning rate. In addition, differences were found between participant groups training with different levels of flight simulator motion fidelity.

In real-life manual control tasks, human controllers are often required to follow a visible and predictable reference signal, enabling them to use feedforward control actions in conjunction with feedback actions that compensate for errors. Little is known about human control behavior in these situations. This paper investigates how humans adapt their feedforward control dynamics to the controlled element dynamics in a combined ramp-tracking and disturbance-rejection task. A human-in-the-loop experiment is performed with a pursuit display and vehicle-like controlled elements, ranging from a single integrator through second-order systems with a break frequency at either 3, 2, or 1 rad/s, to a double integrator. Because the potential benefits of feedforward control increase with steeper ramp segments in the target signal, three steepness levels are tested to investigate their possible effect on feedforward control with the various controlled elements. Analyses with four novel models of the operator, fitted to time-domain data, reveal feedforward control for all tested controlled elements and both (nonzero) tested levels of ramp steepness. For the range of controlled element dynamics investigated, it is found that humans adapt to these dynamics in their feedforward response, with a close to perfect inversion of the controlled element dynamics. No significant effects of ramp steepness on the feedforward model parameters are found.

This paper describes a novel method for analyzing the training effectiveness for skill-based manual control tasks based on multimodal human operator models. For skill-based tracking tasks, it is known that the adopted human operator dynamics can be modeled accurately with multimodal human operator models. In this paper, estimated human operator model parameters are used to explicitly quantify the changes that occur in the operator's use of visual and motion feedback during skill-acquisition and transfer. A quasi-transfer-of-training experiment is described, in which inexperienced participants were trained to perform an aircraft pitch attitude tracking task, either in a fixed-base or in moving-base simulator environment. After the training phase, the participants were transferred to the other simulator setting, to reveal possible transfer effects. Preliminary results from one participant in each experiment group indicate that the fitted models are successful in revealing the changes that occur in the multimodal manual control characteristics of the participants, and show that convergence to a final skill-based control strategy requires significant training. Furthermore, the presented results suggest that there might be limited direct transfer from training in a fixed-base environment to a moving-base environment.

In manual control, a pursuit display may support the use of a multimodal “pursuit” control strategy by the human operator. This paper evaluates two methods that may be used to directly estimate describing functions for such multimodal human operator control dynamics in pursuit tracking. The first is a previously developed frequency-domain method based on Fourier coefficients. The second method makes use of linear-time invariant ARMAX models. An experiment is described in which participants performed tracking tasks with quasi-random multisine target and disturbance forcing functions, for single and double integrator controlled elements and with compensatory and pursuit displays. The experiment data confirms the findings from previous experiments, where it was found that multimodal pursuit control dynamics are adopted in control of systems with double integrator dynamics, but not for single integrator control tasks. Furthermore, the direct multimodal identification methods were found to give improved insight into the internal organization and dynamics of the human operator in pursuit tracking.

In the manual control of a dynamic system, the human controller (HC) often follows a visible and predictable reference path. Compared with a purely feedback control strategy, performance can be improved by making use of this knowledge of the reference. The operator could effectively introduce feedforward control in conjunction with a feedback path to compensate for errors, as hypothesized in literature. However, feedforward behavior has never been identified from experimental data, nor have the hypothesized models been validated. This paper investigates human control behavior in pursuit tracking of a predictable reference signal while being perturbed by a quasi-random multisine disturbance signal. An experiment was done in which the relative strength of the target and disturbance signals were systematically varied. The anticipated changes in control behavior were studied by means of an ARX model analysis and by fitting three parametric HC models: two different feedback models and a combined feedforward and feedback model. The ARX analysis shows that the experiment participants employed control action on both the error and the target signal. The control action on the target was similar to the inverse of the system dynamics. Model fits show that this behavior can be modeled best by the combined feedforward and feedback model.

The human in manual control of a dynamical system can use both feedback and feedforward control strategies and will select a strategy based on performance and required effort. Literature has shown that feedforward control is used during tracking tasks in response to predictable targets. The influence of an external disturbance signal on the utilization of a feedforward control strategy has never been investigated, however. We hypothesized that the human will use a combined feedforward and feedback control strategy whenever the predictable target signal is sufficiently strong, and a predominantly feedback strategy whenever the random disturbance signal is dominant. From the data of a human-in-the-loop experiment we conclude that feedforward control is used in all the considered experimental conditions, including those where the disturbance signal is dominant and feedforward control does not deliver a marked performance advantage.

In continuous manual control tasks, pilots adapt their control strategy to the dynamics of the controlled element to yield adequate performance of the combined pilot-vehicle system. For a controlled element representing the linearized pitch dynamics of a small jet aircraft, the pilot models described in literature were found to lack the required freedom in the pilot equalization term to accurately model the adopted pilot compensation. An additional lead term in the pilot equalization transfer function was found to significantly increase the accuracy in modeling manual control behavior of aircraft pitch dynamics.