Evaluating Simulator Motion Fidelity using In-Flight and Simulator Measurements of Roll Tracking Behavior

Pool, Daan M.; Zaal, Peter; Damveld, HJ; Paassen, M. M. van

doi:10.2514/6.2012-4635

Cited by 18 publications

(61 citation statements)

References 18 publications

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“…Pilots are also not found to compensate for lower magnitude motion feedback (K mf < 1) by a matching increase of their motion response gain, K m . Finally, from the in-flight (IF) data from [155], pilots were found to control with a lower gain during in-flight tracking than for the 1-to-1 simulator motion configuration, a result that might be attributable to other factors than the quality of the supplied motion feedback (e.g., environmental variables, see Fig. 2).…”

Section: B Flight Simulator Motion Cueing Fidelitymentioning

confidence: 99%

“…Fig. 8 shows multi-modal HC modeling results from [155], where seven pilots performed an aircraft roll attitude tracking task both in real flight and in a moving-base flight simulator for a number of different settings of a first-order high-pass motion filter, H mf . In Fig.…”

Section: B Flight Simulator Motion Cueing Fidelitymentioning

confidence: 99%

“…For example, an analytic controltheoretic criterion based on pilot-vehicle system dynamics that is sensitive to variations in motion cueing fidelity [22], [23], [161] has been derived and successfully applied to a range of different aircraft (both fixed-wing and movingwing) and flight maneuvers. In addition, multi-channel HC modeling and identification techniques [79], [81], [83] can be used to explicitly measure pilots responses to visual and (simulator) motion cues during tracking tasks, to discover under which motion washout filter settings pilots change their control behavior [26], [29], [155]. This approach also enables objective quantification of the behavioral discrepancies that occur in flight simulators compared to real flight [18], [162] and helps relate these discrepancies to the choices in motion cueing [29], [155].…”

Section: B Flight Simulator Motion Cueing Fidelitymentioning

confidence: 99%

See 2 more Smart Citations

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

Mulder

Pool

Abbink

et al. 2018

IEEE Trans. Human-Mach. Syst.

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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.

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Section: B Flight Simulator Motion Cueing Fidelitymentioning

confidence: 99%

Section: B Flight Simulator Motion Cueing Fidelitymentioning

confidence: 99%

Section: B Flight Simulator Motion Cueing Fidelitymentioning

confidence: 99%

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

Mulder

Pool

Abbink

et al. 2018

IEEE Trans. Human-Mach. Syst.

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“…Recent experiments have shown that the human operator does not utilize additional motion feedback that has been distorted through application of high-pass motion filters. 16,17 Similarly, investigations including ab initio training experiments 18,19 revealed that the human operator might only develop an additional motion feedback response after extensive training. Also, Pool et al 16 report only modest contributions of motion to operator control behavior in tasks with low-fidelity motion feedback, which even cast doubts on the presence of an additional motion feedback response.…”

Section: Introductionmentioning

confidence: 99%

“…16,17 Similarly, investigations including ab initio training experiments 18,19 revealed that the human operator might only develop an additional motion feedback response after extensive training. Also, Pool et al 16 report only modest contributions of motion to operator control behavior in tasks with low-fidelity motion feedback, which even cast doubts on the presence of an additional motion feedback response. Selecting a multi-channel feedback organization in these cases could potentially lead to the identification of incorrect human operator dynamics.…”

Section: Introductionmentioning

confidence: 99%

Objective ARX Model Order Selection for Multi-Channel Human Operator Identification

Roggenkämper

Pool

Drop

et al. 2016

AIAA Modeling and Simulation Technologies Conference

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In manual control, the human operator primarily responds to visual inputs but may elect to make use of other available feedback paths such as physical motion, adopting a multi-channel control strategy. Human operator identification procedures generally require a priori selection of the model structure, which can be problematic as the exact feedback organization operators adopt is not always clear in advance. This paper evaluates a novel method for objectively detecting the presence of additional human operator feedback responses in control tasks with multiple inputs. The approach makes use of linear-time invariant ARX models for system identification, combined with an objective model selection criterion. To test the method, an experiment was conducted in which participants performed a compensatory yaw attitude tracking task in a moving-base flight simulator, with varying motion cueing settings. In addition, a pursuit tracking condition without motion feedback was tested. For all conditions, the objective ARX model-based identification method was used to verify the presence of a possible additional human operator output feedback response. With appropriate tuning of the penalty on model complexity in the model selection criterion, the methodology proved successful in correctly identifying the additional operator responses in experimental conditions that contained no motion or high-quality motion feedback. With low-fidelity motion feedback or a pursuit display, the results suggest that no consistent feedback response is achieved by the participants. The approach was substantiated with offline Monte Carlo simulations, which show strong correlation with the obtained experiment results.

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An Improved Washout Algorithm for UPRT Scenario

Tang

Wang

2021

Engineering Psychology and Cognitive Ergonomics

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Evaluating Simulator Motion Fidelity using In-Flight and Simulator Measurements of Roll Tracking Behavior

Cited by 18 publications

References 18 publications

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

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

Objective ARX Model Order Selection for Multi-Channel Human Operator Identification

An Improved Washout Algorithm for UPRT Scenario

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