Human control strategies in pursuit tracking with a disturbance input

Yu, Bo; Gillespie, R. Brent; Freudenberg, J.S.; Cook, J.A.

doi:10.1109/cdc.2014.7039980

Cited by 15 publications

(19 citation statements)

References 11 publications

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“…Our results suggest that participants use both feedback and feedforward control to continuously track reference trajectories and reject disturbances, consistent with previous results for first-order [1], [4], [5], [7] and fourth-order [6], [25] systems, lending further support for Hypothesis 1.…”

Section: A Combined Feedback and Feedforward Improved Predictionsupporting

confidence: 90%

“…5). Predictions for both models were better ( R 2 closer to 1) below crossover frequency (0.25 Hz, determined as the lowest stimulated frequency where the gain of the open-loop transfer function is less than 1 [2], [6]), and decreased in accuracy ( R 2 closer to 0) at higher frequencies, suggesting that the linear system models developed are more accurate at lower frequencies. There was no significant difference in prediction accuracy between the B and B + F models for any specific frequency ( p > 0.05).…”

Section: Resultsmentioning

confidence: 99%

“…3,pg. 4] system M , humans learn to behave like an LTI system for a range of reference and disturbance signals [2], [4], [5]. Therefore, the human's control u produced in response to reference r and output y satisfies the law of superposition, H(r, y) = H(r, 0) + H(0, y).…”

Section: A Combined Feedback and Feedforward Improves Predictionmentioning

confidence: 99%

“…3, pg. 4] system M , we assume that people behave approximately like LTI transormations for a range of reference and disturbance signals [2], [4]–[6], [9], [21]. When this assumption holds, the control signal u produced by the human in response to reference r can be manipulated to express the empirical and prescribed and disturbance d satisfies the law of superposition, where T ur and T ud are LTI transformations.…”

Section: Introductionmentioning

confidence: 99%

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Effect of Handedness on Learned Controllers and Sensorimotor Noise During Trajectory-Tracking

Yamagami

Peterson

Howell

et al. 2020

Preprint

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In human-in-the-loop control systems, operators can learn to manually control dynamic machines with either hand using a combination of reactive (feedback) and predictive (feedforward) control. This paper studies the effect of handedness on learned controllers and performance during a continuous trajectory-tracking task. In an experiment with 18 participants, subjects perform an assay of unimanual trajectory-tracking and disturbance-rejection tasks through second-order machine dynamics, first with one hand then the other. To assess how hand preference (or dominance) affects learned controllers, we extend, validate, and apply a non-parametric modeling method to estimate the concurrent feedback and feedforward elements of subjects' controllers. We find that handedness does not affect the learned controller and that controllers transfer between hands. Observed improvements in time-domain tracking performance may be attributed to adaptation of feedback to reject disturbances arising exogenously (i.e. applied by the experimenter) and endogenously (i.e. generated by sensorimotor noise)

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Section: A Combined Feedback and Feedforward Improved Predictionsupporting

confidence: 90%

Section: Resultsmentioning

confidence: 99%

Section: A Combined Feedback and Feedforward Improves Predictionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Effect of Handedness on Learned Controllers and Sensorimotor Noise During Trajectory-Tracking

Yamagami

Peterson

Howell

et al. 2020

Preprint

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“…that feedforward control was studied by means of system identification and parameter estimation techniques [17]- [19] with the goal of modeling the feedforward in detail.…”

mentioning

confidence: 99%

Objective Model Selection for Identifying the Human Feedforward Response in Manual Control

Drop

Pool

Paassen

et al. 2018

IEEE Trans. Cybern.

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Abstract-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 ARX 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-theloop 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.

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Sensorimotor delays in tracking may be compensated by negative feedback control of motion-extrapolated position

et al. 2020

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Sensorimotor delays dictate that humans act on outdated perceptual information. As a result, continuous manual tracking of an unpredictable target incurs significant response delays. However, no such delays are observed for repeating targets such as the sinusoids. Findings of this kind have led researchers to claim that the nervous system constructs predictive, probabilistic models of the world. However, a more parsimonious explanation is that visual perception of a moving target position is systematically biased by its velocity. The resultant extrapolated position could be compared with the cursor position and the difference canceled by negative feedback control, compensating sensorimotor delays. The current study tested whether a position extrapolation model fit human tracking of sinusoid (predictable) and pseudorandom (less predictable) targets better than the non-biased position control model, Twenty-eight participants tracked these targets and the two computational models were fit to the data at 60 fixed loop delay values (simulating sensorimotor delays). We observed that pseudorandom targets were tracked with a significantly greater phase delay than sinusoid targets. For sinusoid targets, the position extrapolation model simulated tracking results more accurately for loop delays longer than 120 ms, thereby confirming its ability to compensate for sensorimotor delays. However, for pseudorandom targets, this advantage arose only after 300 ms, indicating that velocity information is unlikely to be exploited in this way during the tracking of less predictable targets. We conclude that negative feedback control of position is a parsimonious model for tracking pseudorandom targets and that negative feedback control of extrapolated position is a parsimonious model for tracking sinusoidal targets.

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Human control strategies in pursuit tracking with a disturbance input

Cited by 15 publications

References 11 publications

Effect of Handedness on Learned Controllers and Sensorimotor Noise During Trajectory-Tracking

Effect of Handedness on Learned Controllers and Sensorimotor Noise During Trajectory-Tracking

Objective Model Selection for Identifying the Human Feedforward Response in Manual Control

Sensorimotor delays in tracking may be compensated by negative feedback control of motion-extrapolated position

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