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F.M. Drop
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Manual Control with Pursuit Displays
New Insights, New Models, New Issues
Mathematical control models are widely used in tuning manual control systems and understanding human performance. The most common model, the crossover model, is severely limited, however, in describing realistic human control behaviour in relevant control tasks as it is only valid for tracking with a compensatory display. This paper first discusses the state-of-the-art in modelling human control in tracking with pursuit displays. It is shown that, although both tasks seem very similar, the separate presentation of target and system output signals allows operators to adopt a huge variety in control strategies, which makes the development of a universal model for pursuit control a challenge. Two recent models are then described which can act as precursors to such a universal model. Third, system identification choices and issues are discussed for pursuit tracking tasks. Finally, it is argued that it is inevitable that time-varying rather than time-invariant methods are needed to properly describe human behaviour in the pursuit tracking task, as skilled operators will learn to characterize the probabilistic nature of the task, which cannot be captured in a single, linear, time-invariant model.
...
Mathematical control models are widely used in tuning manual control systems and understanding human performance. The most common model, the crossover model, is severely limited, however, in describing realistic human control behaviour in relevant control tasks as it is only valid for tracking with a compensatory display. This paper first discusses the state-of-the-art in modelling human control in tracking with pursuit displays. It is shown that, although both tasks seem very similar, the separate presentation of target and system output signals allows operators to adopt a huge variety in control strategies, which makes the development of a universal model for pursuit control a challenge. Two recent models are then described which can act as precursors to such a universal model. Third, system identification choices and issues are discussed for pursuit tracking tasks. Finally, it is argued that it is inevitable that time-varying rather than time-invariant methods are needed to properly describe human behaviour in the pursuit tracking task, as skilled operators will learn to characterize the probabilistic nature of the task, which cannot be captured in a single, linear, time-invariant model.
Journal article
(2018)
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Frank M. Drop, Daan M. Pool, Marinus M. van Paassen, Max Mulder, Heinrich H. Bulthoff
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: 1) a single integrator (SI); 2) a second-order system; and 3) 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 SI 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.
...
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: 1) a single integrator (SI); 2) a second-order system; and 3) 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 SI 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.
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 autoregressive with exogenous input 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-the-loop 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.
...
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 autoregressive with exogenous input 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-the-loop 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.
Manual control cybernetics
State-of-the-art and current trends
Journal article
(2017)
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Max Mulder, Daan M. Pool, David A. Abbink, Erwin R. Boer, Peter M.T. Zaal, Frank M. Drop, Kasper van der El, Marinus M. van Paassen
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 semiautomatic control.
...
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 semiautomatic control.
Doctoral thesis
(2016)
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Frank Drop
Understanding how humans control a vehicle (cars, aircraft, bicycles, etc.) enables engineers to design faster, safer, more comfortable, more energy efficient, more versatile, and thus better vehicles. In a typical control task, the Human Controller (HC) gives control inputs to a vehicle such that it follows a particular reference path (e.g., the road) accurately. The HC is simultaneously required to attenuate the effect of disturbances (e.g., turbulence) perturbing the intended path of the vehicle. To do so, the HC can use a control organization that resembles a closed-loop feedback controller, a feedforward controller, or a combination of both. Previous research has shown that a purely closed-loop feedback control organization is observed only in specific control tasks, that do not resemble realistic control tasks, in which the information presented to the human is very limited. In realistic tasks, a feedforward control strategy is to be expected; yet, almost all previously available HC models describe the human as a pure feedback controller lacking the important feedforward response. Therefore, the goal of the research described in this thesis was to obtain a fundamental understanding of feedforward in human manual control.
First, a novel system identification method was developed, which was necessary to identify human control dynamics in control tasks involving realistic reference signals. Second, the novel identification method was used to investigate three important aspects of feedforward through human-in-the-loop experiments which resulted in a control-theoretical model of feedforward in manual control. The central element of the feedforward model is the inverse of the vehicle dynamics, equal to the theoretically ideal feedforward dynamics. However, it was also found that the HC is not able to apply a feedforward response with these ideal dynamics, and that limitations in the perception, cognition, and action loop need to be modeled by additional model elements: a gain, a time delay, and a low-pass filter.
Overall, the thesis demonstrated that feedforward is indeed an essential part of human manual control behavior and should be accounted for in many human-machine applications.
...
Understanding how humans control a vehicle (cars, aircraft, bicycles, etc.) enables engineers to design faster, safer, more comfortable, more energy efficient, more versatile, and thus better vehicles. In a typical control task, the Human Controller (HC) gives control inputs to a vehicle such that it follows a particular reference path (e.g., the road) accurately. The HC is simultaneously required to attenuate the effect of disturbances (e.g., turbulence) perturbing the intended path of the vehicle. To do so, the HC can use a control organization that resembles a closed-loop feedback controller, a feedforward controller, or a combination of both. Previous research has shown that a purely closed-loop feedback control organization is observed only in specific control tasks, that do not resemble realistic control tasks, in which the information presented to the human is very limited. In realistic tasks, a feedforward control strategy is to be expected; yet, almost all previously available HC models describe the human as a pure feedback controller lacking the important feedforward response. Therefore, the goal of the research described in this thesis was to obtain a fundamental understanding of feedforward in human manual control.
First, a novel system identification method was developed, which was necessary to identify human control dynamics in control tasks involving realistic reference signals. Second, the novel identification method was used to investigate three important aspects of feedforward through human-in-the-loop experiments which resulted in a control-theoretical model of feedforward in manual control. The central element of the feedforward model is the inverse of the vehicle dynamics, equal to the theoretically ideal feedforward dynamics. However, it was also found that the HC is not able to apply a feedforward response with these ideal dynamics, and that limitations in the perception, cognition, and action loop need to be modeled by additional model elements: a gain, a time delay, and a low-pass filter.
Overall, the thesis demonstrated that feedforward is indeed an essential part of human manual control behavior and should be accounted for in many human-machine applications.
In the manual control of a dynamic system, the human controller (HC) is often required to follow a visible and predictable reference path. Using the predictable aspect of a reference signal, through applying feedforward control, the HC can significantly improve performance as compared to a purely feedback control strategy. A proper definition of a signal’s predictability, however, is never given in literature. This paper investigates the predictability of a sum-of-sinusoids target signal, as a function of the number of sinusoid components and the fact whether the sinusoid frequencies are harmonic, or not. A human-in-the-loop experiment was done, with target signals varying for these two signal characteristics. A combined feedback-feedforward HC model was identified and parameters were estimated. It was found that for all experimental conditions, subjects used a feedforward strategy. Results further showed thatsubjects were able to perform better for harmonic signals as compared to non-harmonic signals, for signals with roughly the same frequency content.
...
In the manual control of a dynamic system, the human controller (HC) is often required to follow a visible and predictable reference path. Using the predictable aspect of a reference signal, through applying feedforward control, the HC can significantly improve performance as compared to a purely feedback control strategy. A proper definition of a signal’s predictability, however, is never given in literature. This paper investigates the predictability of a sum-of-sinusoids target signal, as a function of the number of sinusoid components and the fact whether the sinusoid frequencies are harmonic, or not. A human-in-the-loop experiment was done, with target signals varying for these two signal characteristics. A combined feedback-feedforward HC model was identified and parameters were estimated. It was found that for all experimental conditions, subjects used a feedforward strategy. Results further showed thatsubjects were able to perform better for harmonic signals as compared to non-harmonic signals, for signals with roughly the same frequency content.
The human controller (HC) can greatly improve target-tracking performance by utilizing a feedforward operation on the target signal, in addition to a feedback response. System identification methods are used to determine the correct HC model structure: purely feedback or a combined feedforward/feedback model. In this paper, we investigate three central issues that complicate this objective. First, the identification method should not require prior assumptions regarding the dynamics of the feedforward and feedback components. Second, severe biases might be introduced by high levels of noise in the data measured under closed-loop conditions. To address the first two issues, we will consider two identification methods that make use of linear ARX models: the classic direct method and the two-stage indirect method of van den Hof and Schrama (1993). Third, model complexity should be considered in the selection of the ‘best’ ARX model to prevent ‘false-positive’ feedforward identification. Various model selection criteria, that make an explicit trade-off between model quality and model complexity, are considered. Based on computer simulations with a HC model, we conclude that 1) the direct method provides more accurate estimates in the frequency range of interest, and 2) existing model selection criteria do not prevent false-positive feedforward identification.
...
The human controller (HC) can greatly improve target-tracking performance by utilizing a feedforward operation on the target signal, in addition to a feedback response. System identification methods are used to determine the correct HC model structure: purely feedback or a combined feedforward/feedback model. In this paper, we investigate three central issues that complicate this objective. First, the identification method should not require prior assumptions regarding the dynamics of the feedforward and feedback components. Second, severe biases might be introduced by high levels of noise in the data measured under closed-loop conditions. To address the first two issues, we will consider two identification methods that make use of linear ARX models: the classic direct method and the two-stage indirect method of van den Hof and Schrama (1993). Third, model complexity should be considered in the selection of the ‘best’ ARX model to prevent ‘false-positive’ feedforward identification. Various model selection criteria, that make an explicit trade-off between model quality and model complexity, are considered. Based on computer simulations with a HC model, we conclude that 1) the direct method provides more accurate estimates in the frequency range of interest, and 2) existing model selection criteria do not prevent false-positive feedforward identification.
Conference paper
(2016)
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N Roggenkämper, DM Pool, Frank Drop, Rene van Paassen, M Mulder
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. Hu- man 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 pa- per 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 mod- els 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 ap- propriate 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.
...
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. Hu- man 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 pa- per 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 mod- els 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 ap- propriate 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.