D.M. Pool

Modeling Human Multimodal Perception and Control Using Genetic Maximum Likelihood Estimation

This paper presents a new method for estimating the parameters of multi-channel pilot models that is based on maximum likelihood estimation. To cope with the inherent nonlinearity of this optimization problem, the gradient-based Gauss-Newton algorithm commonly used to optimize the likelihood function in terms of output error is complemented with a genetic algorithm. This significantly increases the probability of finding the global optimum of the optimization problem. The genetic maximum likelihood method is successfully applied to data from a recent human-in-the-loop experiment. Accurate estimates of the pilot model parameters and the remnant characteristics were obtained. Multiple simulations with increasing levels of pilot remnant were performed, using the set of parameters found from the experimental data, to investigate how the accuracy of the parameter estimate is affected by increasing remnant. It is shown that only for very high levels of pilot remnant the bias in the parameter estimates is substantial. Some adjustments to the maximum likelihood method are proposed to reduce this bias.

Use of Pitch and Heave Motion Cues in a Pitch Control Task

During pitch rotation of the aircraft, a pilot, seated in front of the aircraft center of gravity, is subjected to rotational pitch and vertical heave motion. The heave motion is a combination of the vertical motion of the aircraft center of gravity and the heave motion as a result of the pitch rotation. In a pitch tracking task, all of these cues could potentially have a positive effect on performance and control behavior, as they are all related to the aircraft pitch attitude. To improve the tuning of flight simulator motion filters, a better understanding of how these motion components are used by the pilot is required. First, the optimal use of the different motion components was evaluated using an optimal control analysis. Next, an aircraft pitch attitude control experiment was performed in the SIMONA Research Simulator, investigating the effects of pitch rotation, pitch heave, and center of gravity heave on pilot control behavior. Pilot performance significantly improved with pitch motion, with an increased crossover frequency for the disturbance open loop. The increase in performance was a result of an increased visual gain and a reduction in visual lead, allowed for by the addition of pitch motion. Pitch heave motion showed similar but smaller effects. The center of gravity heave motion, although taking up most of the simulator motion space, was found to have no significant effects on performance and control behavior.

Effects of Heave Washout Settings in Aircraft Pitch Disturbance Rejection

In most moving-base flight simulators, the simulated aircraft motion needs to be filtered with motion washout filters to keep the simulator within its limited motion envelope. Translational motion in particular requires filtering, as the low frequency components of the vehicle motion tend to quickly drive simulators toward their motion bounds. Commonly, linear washout filters are therefore used to attenuate the simulated motion in magnitude and in phase. It is found in many studies that the settings of these washout filters affect pilot performance and control behavior. In most of these studies, no comparison to a case with one-to-one motion cues is performed, as a result of the limited motion envelope of the used simulators. In the current study, an experiment was performed in the SIMONA Research Simulator at Delft University of Technology to investigate the effects of heave washout settings on pilot performance and control behavior in a pitch attitude control task. In addition to rotational pitch motion, heave accelerations at the pilot station that result directly from aircraft pitch were evaluated. This heave motion component could be supplied one-to-one in the simulator due to the modest size of the aircraft model, a Cessna Citation I business jet. The experiment revealed that pilot performance and control activity both increased significantly with increasing heave motion fidelity. An analysis of pilot control behavior using pilot models indicated that the enhanced performance was caused by an increase in the magnitude with which pilots responded to visual and physical motion stimuli and a decrease in the amount of visual lead that was generated by the pilots.

Effects of Peripheral Visual and Physical Motion Cues in Roll-Axis Tracking Tasks

In this paper, the effects of peripheral visual and physical motion cues on manual control of second-order roll dynamics are investigated. In particular, the differences between the use of these cues in compensatory target-following and disturbance-rejection tasks are considered. Tracking performance, control activity, and measures of control behavior are determined from recent measurements and compared with results from an earlier experiment. Most previously reported effects of peripheral visual and physical motion cues in target following and disturbance rejection are confirmed. A comparison of tasks with varying levels of difficulty is found to reveal reduced effectiveness of peripheral visual and physical motion cues in the less difficult target-following tasks only. Observed differences in measured control behavior for target following and disturbance rejection are related to effective strategies for reducing tracking errors introduced by the forcing-function signals in both tasks.

Multimodal Pilot Control Behavior in Combined Target-Following Disturbance-Rejection Tasks

Investigating how humans use their perceptual modalities while controlling a vehicle is important for the design of new control systems and the optimization of simulator motion cueing. For the identification of separate pilot response functions to the different perceived cues, multiple forcing functions need to be inserted into the manual control loop. An example of a task with multiple forcing functions is a combined target-following disturbance-rejection task, where a target and disturbance signal are used to separate the human visual and vestibular motion responses. The use of multiple forcing functions, however, also affects the nature of the control task and how the motion cues are used by the pilot to form a proper control action. This paper presents the results of an experiment where possible effects of using multiple forcing functions on pilot control behavior in an aircraft pitch control task are investigated. The results indicate that pilot performance and control activity are significantly lower when the relative power of the target forcing function is increased. This is caused by a significant change in multimodal pilot control behavior. With an increase in relative target power, the visual-perception gain is reduced and the visual time delay becomes higher. The motion-perception gain reduces if both forcing functions have significant power. It is also found that multimodal pilot control behavior in a pure target or disturbance task can be analyzed by adding a small additional disturbance or target signal, respectively. In this case, the effects on control behavior are found to be minimal, while still being able to accurately estimate the parameters of the multichannel pilot model.

Identification of the Feedforward Component in Manual Control With Predictable Target Signals

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.

Pilot equalization in manual control of aircraft dynamics

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.

Modeling Wide-Frequency-Range Pilot Equalization for Control of Aircraft Pitch Dynamics

In continuous manual control tasks, human controllers adapt their control strategy to the dynamics of the controlled element. This compensation for the controlled-element dynamics is performed around the pilot–vehicle system crossover frequency, in order to obtain satisfactory performance of the combined pilot–vehicle system, but is also seen to extend to frequencies well above crossover. For a controlled element representing the linearized pitch dynamics of a small conventional jet aircraft, an extension to the models for pilot equalization described in the literature was found to be needed for the modeling of the adopted pilot equalization dynamics over a wide frequency range. Measured pilot describing functions revealed that pilots use a combination of low-frequency lag and high-frequency lead equalization to compensate for the characteristics of these typical aircraft pitch dynamics around the short-period mode. An additional high-frequency lead term in the pilot equalization transfer function was found to allow for the modeling of these adopted equalization dynamics over a wide frequency range, thereby also yielding a significant increase in the percentage of measured control inputs that is explained by the pilot model. Furthermore, for this controlled element the extended model for the equalization dynamics was found to be important for the interpretation of the changes in pilot control behavior that occur due to the presence of physical motion feedback.

Effects of Simulator Motion Feedback on Training of Skill-Based Control Behavior

This paper presents the results of a quasi-transfer-of-training experiment performed in the SIMONA Research Simulator at Delft University of Technology. The goal of the experiment was to quantify the effects of simulator motion feedback on the training of skill-based human operator control behavior using multimodal human operator modeling techniques. In the experiment, 24 task-naive participants, divided over two groups, were trained in performing a skill-based compensatory pitch tracking task. The first group was trained in a fixed-base setting and transferred to a moving-base condition; the second group trained with motion feedback and then transferred to the fixed-base condition. The group that received initial moving-base training showed quick adaptation of their control behavior upon transfer to the fixed-base setting and limited further learning. The group that trained in the fixed-base condition showed only limited transfer of their learned control strategy to the moving-base setting. After transfe...

Pitch Motion Perception Thresholds During Passive and Active Tasks

Knowledgeaboutmotionperception thresholds isessential forsimulator motioncueing. Thresholdsare generally measured in a passive experimental setup in which subjects do not actively influence their motion. For flight simulation applications, itis usefulto also investigatethresholds during control tasks, where pilotsactively influence the motion they sense. In this paper, thresholds were estimated during an active control task using a pilot model parameter identification method. A comparison with conventional passive threshold measurements was made. The threshold identification method was based on a multichannel pilot model extended with a nonlinear absolute threshold element. Two experiments were performed in a flight simulator: a passive experiment to measure the sensory pitch threshold, and an active experiment with a compensatory control task to identify the active pitch threshold. In the active experiment, the gain of the inertial motion amplitude was varied and two types of compensatorycontroltaskswereconsidered.Forbothtasks,thepitchthresholdwasidentifiableonlyforhighmotion gainlevels.Themeasuredpassivethresholdwaslowerthanothervaluesfoundinliterature.Thethresholdidentified from the active control task was higher than the measured passive threshold, but it was comparable with passive threshold values reported in other studies.