The Predictability of a Target Signal Affects Manual Feedforward Control

AIAA Scitech 2020 Forum

2020

This paper presents the results of a training and retention experiment conducted to objectively and quantitatively evaluate the acquisition, decay, and retention of skill-based manual control behavior in a compensatory dual-axis roll and pitch attitude tracking task. In this study, thirty-eight fully task-naive participants were trained in a fixed-base setting in the Human-Machine Interaction Laboratory at Delft University of Technology and subsequently divided into three matched groups based on their training performance and control behavior. Performance of the first group was re-evaluated after a period of non-practice of six months, whereas the second group was retested at both three and six months after training, and skill retention of the third group was measured after two, four, and six months. The goal of the experiment was to model the decay curve of skill-based manual control behavior and to determine the re-acquisition rate of lost skills compared to their initial acquisition rate. To explicitly quantify changes in manual control skills, learning curve models were fitted to metrics of task performance and control activity. The results suggest that control skills decay following a negatively accelerating decay curve and that lost skills are re-acquired at a higher rate than their initial development rate.

Section: Forcing Functionsmentioning

confidence: 99%

Retention of Manual Control Skills in Multi-Axis Tracking Tasks

Wijlens

Zaal

AIAA Scitech 2020 Forum

2020

“…Still, a broad collection of empirical observations and recent data support the SOP's precognitive phase. For example, numerous studies report notably improved tracking performance when following 'predictable' target target signals, in comparison with 'unpredictable' signals [72], [106], [108], [117]- [120], even for signals with equivalent frequency content and bandwidth. Further evidence for the development of a precognitive control mode has been found with observed response time delays and phase lags that are smaller than a 'normal' human reaction time [108], [121].…”

Section: Precognitive Controlmentioning

confidence: 99%

“…For instance, in [120] HCs were asked to track three pairs of "harmonic" (H) and "non-harmonic" (NH) multisine signals, consisting of 2, 3, or 4 sinusoids with a pursuit display. Analytical analysis with a (linear) HC model as shown in Fig.…”

Section: B Feedforward On Predictable Target Signalsmentioning

confidence: 99%

Manual Control Cybernetics: State-of-the-Art and Current Trends

Mulder

IEEE Trans. Human-Mach. Syst.

Abbink

et al. 2018

Self Cite

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 approach has limited applicability, because the actual control strategy is different from the HC model; thus, the model needs further improvements. An important, but poorly understood aspect of feedforward behavior that needs to be addressed in future work is the predictability of the target signal [15], [24].…”

Section: Discussionmentioning

confidence: 99%

Effects of Target Signal Shape and System Dynamics on Feedforward in Manual Control

Drop

Paassen

et al. 2019

IEEE Trans. Cybern.

Self Cite

Abstract-The human controller (HC) in manual control of a dynamical system often follows a visible and predictable reference path (target). The HC can adopt a control strategy combining closed-loop feedback and an open-loop feedforward response. The effects of the target signal waveform shape and the system dynamics on the human feedforward dynamics are still largely unknown, even for common, stable, vehicle-like dynamics. This paper studies the feedforward dynamics through computer model simulations and compares these to system identification results from human-in-the-loop experimental data. Two target waveform shapes are considered, constant velocity ramp segments and constant acceleration parabola segments. Furthermore, three representative vehicle-like system dynamics are considered: a single integrator, a second-order system, and a double integrator. The analyses show that the HC utilizes a combined feedforward/feedback control strategy for all dynamics with the parabola target, and for the single integrator and second-order system with the ramp target. The feedforward model parameters are, however, very different between the two target waveform shapes, illustrating the adaptability of the HC to task variables. Moreover, strong evidence of anticipatory control behavior in the HC is found for the parabola target signal. The HC anticipates the future course of the parabola target signal given extensive practice, reflected by negative feedforward time delay estimates.