Modeling Human Multichannel Perception and Control Using Linear Time-Invariant Models

Nieuwenhuizen, Frank M.; Zaal, Peter; Mulder, M.; Mulder, J.A.

doi:10.2514/1.32307

Cited by 78 publications

(82 citation statements)

References 11 publications

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“…In this additional step, Fourier coefficients (FC) or linear time-invariant (LTI) models (e.g., autoregressive exogeneous (ARX) models) are used to estimate non-parametric frequency response functions. 8,11 These non-parametric frequency responses are then used in a second step, in which a multi-channel pilot model is fit by adjusting its parameters. For estimating the pilot model parameters a frequency-domain criterion is used, which results in a nonlinear optimization problem.…”

Section: Iid1 Frequency-domain Techniquesmentioning

confidence: 99%

“…[3][4][5][6][7][8][9] Human manual vehicle control behavior is an inherently nonlinear and time-varying closed-loop process. For carefully designed control tasks, however, the control behavior of well-trained individuals can be accurately described with quasi-linear pilot models.…”

mentioning

confidence: 99%

“…8 An alternative to these frequency-domain parameter estimation methods are time-domain identification procedures, which allow for estimation of model parameters from time-domain data directly. For such time-domain identification techniques, the requirements to ensure the identifiability of a model are significantly less stringent.…”

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confidence: 99%

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Modeling Human Multimodal Perception and Control Using Genetic Maximum Likelihood Estimation

Zaal

Pool

Chu

et al. 2009

Journal of Guidance, Control, and Dynamics

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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

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Section: Iid1 Frequency-domain Techniquesmentioning

confidence: 99%

mentioning

confidence: 99%

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Modeling Human Multimodal Perception and Control Using Genetic Maximum Likelihood Estimation

Zaal

Pool

Chu

et al. 2009

Journal of Guidance, Control, and Dynamics

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“…In order to identify and model the multi-channel human operator response, characterized by H pe and H p φ , the disturbance signal f d and the target signal f d were independent sum-of-sines signals. [17][18][19] Given the quasilinear human operator model used, the control input had contributions from the error response, u e , the roll response, u φ , and a remnant n accounting for nonlinear behavior and measurement noise.…”

Section: Iia Control Taskmentioning

confidence: 99%

Effects of Peripheral Visual Cues in Simulator-Based Training of Multimodal Control Skills

Mendes

Pool

Paassen

2017

AIAA Modeling and Simulation Technologies Conference

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This paper describes a training experiment performed in the SIMONA Research Simulator at Delft University of Technology to evaluate the effectiveness of peripheral visual cues as a substitute for physical motion feedback during the development of multimodal control skills. Twenty task-naive participants were divided in two experimental groups and performed a skill-based compensatory roll tracking task. Both groups were trained in a fixed-base setting, but one group was provided with additional out-of-the-window peripheral visual (roll rate) cues. After training, participants were transferred to a moving-base condition where pure roll motion cues were provided. The development of skills in both groups throughout the experiment was studied and compared using fits of a multimodal human operator model with an error and a roll feedback response, to explicitly quantify operators' use of all supplied feedbacks. As expected, the group that had access to peripheral visual cues attained better tracking performance during training and showed the development of multimodal control behavior. However, no evident transfer of this developed multimodal control strategy to a motion setting was observed. This suggests that training with peripheral visual cues is not an effective substitute for training the multimodal skills human operators use when motion feedback is available.

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“…[1][2][3][4] In order for these techniques to provide accurate identification results, specific requirements are posed on the design of experiments in which human control behavior is estimated. These requirements ensure that the human controller behaves like a stationary control element in the control loop.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Time-Varying Pilot Model Parameters

Zaal

Sweet

2011

AIAA Modeling and Simulation Technologies Conference

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Human control behavior is rarely completely stationary over time due to fatigue or loss of attention. In addition, there are many control tasks for which human operators need to adapt their control strategy to vehicle dynamics that vary in time. In previous studies on the identification of time-varying pilot control behavior wavelets were used to estimate the time-varying frequency response functions. However, the estimation of time-varying pilot model parameters was not considered. Estimating these parameters can be a valuable tool for the quantification of different aspects of human time-varying manual control. This paper presents two methods for the estimation of time-varying pilot model parameters, a two-step method using wavelets and a windowed maximum likelihood estimation method. The methods are evaluated using simulations of a closed-loop control task with time-varying pilot equalization and vehicle dynamics. Simulations are performed with and without remnant. Both methods give accurate results when no pilot remnant is present. The wavelet transform is very sensitive to measurement noise, resulting in inaccurate parameter estimates when considerable pilot remnant is present. Maximum likelihood estimation is less sensitive to pilot remnant, but cannot detect fast changes in pilot control behavior.

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Modeling Human Multichannel Perception and Control Using Linear Time-Invariant Models

Cited by 78 publications

References 11 publications

Modeling Human Multimodal Perception and Control Using Genetic Maximum Likelihood Estimation

Modeling Human Multimodal Perception and Control Using Genetic Maximum Likelihood Estimation

Effects of Peripheral Visual Cues in Simulator-Based Training of Multimodal Control Skills

Estimation of Time-Varying Pilot Model Parameters

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