2012Unpublished venueRequires access

Identification of the transition from compensatory to feedforward behavior in manual control

Frank M. Drop, Daan M. Pool, Herman Damveld, M. M. van Paassen, HH Bülthoff, Max Mulder

Open publisher page 4 citations

Abstract

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.

About this research paper

What this paper is about

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.

Why it matters

OpenAlex reports 4 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

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.

Key concepts: Feed forward, Control theory (sociology), SIGNAL (programming language), Computer science, Control (management), Feedforward neural network, Feedback control, Identification (biology)

Related papers

Back to paper searchBrowse research topicsOriginal source
Identification of the transition from compensatory to feedforward behavior in manual control — Research Paper | ScholarLens