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 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-inthe-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
Abstract-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 semi-automatic control.
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.
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 simulators used. In the current study, an experiment was performed in the SIMONA Research Simulator at the 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. Nomenclature A = sinusoid amplitude, deg a z cg = c.g. heave acceleration, m s 2 a z = pitch-heave acceleration, m s 2 e = tracking error signal, deg f d = disturbance forcing function, deg f t = target forcing function, deg H nm = neuromuscular system dynamics H ol = open-loop response H sc = semicircular canal dynamics H p e = pilot visual response H p az = pilot heave motion response H p = pilot pitch motion response Hj! = frequency response function Hs = transfer function H ; e = controlled system dynamics j = imaginary unit, -K = gain, -K m = motion perception gain, -K v = visual perception gain, -k = sinusoid index, -l = pitch-heave arm length, m N = number of points, -n = forcing function frequency integer factor, -S = power spectral density s = Laplace variable T I = visual lag time constant, s T L = visual lead time constant, s T sc 1 , T sc 2 , T sc 3 = semicircular canal model time constants, s t = time, s u = pilot control signal, deg z = vertical position, m Symbols e = elevator deflection, deg = damping factor, -nm = neuromuscular damping, -= pitch angle, deg 2 = variance m = motion perception time delay, s v = visual perception time delay, s = sinusoid phase shift, rad ' m = phase margin, deg ! = frequency, rad s 1 ! c = crossover frequency, rad s 1 ! nm = neuromuscular frequency, rad s 1 ! sp = short period frequency, rad s 1 Subscri...
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.
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.
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