Keywords: Human Machine Systems
Abstract: Mathematical human control models are widely used in tuning manual control systems and understanding human performance. Human behavior is commonly described using linear time-invariant models, averaging-out all non-linear and time-varying effects, which are gathered into the remnant. These models are limited in their capability to capture particular tracking strategies that an experienced subject may learn to use. In this paper, we consider manual control from a different perspective, namely through investigating the probability densities of the tracking error for different regions of the target signal amplitude. Results show that distinct strategies become apparent for compensatory, pursuit and preview tracking tasks. Effects of these strategies are often averaged-out by current models and can only be captured in situation-dependent models. Modeling this systematic human adaptation not captured in linear models could potentially lead to better model fits and explain/reduce part of the remnant.

Keywords: Human Machine Systems
Abstract: Cyberneticists develop mathematical human control models which are used to tune manual control systems and understand human performance limits. Neuroscientists explore the physiology and circuitry of the central nervous system to understand how the brain works. Both research human visuomotor control tasks, such as the pursuit tracking task. In this paper we discuss some commonalities and differences in both approaches to better understand the adapting human controller. Special attention is given to Adaptive Model Theory, which studied adaptive human control using several linear and nonlinear control engineering techniques. The insights gained yield schemes and concepts which pave the way for key future work on this topic.

Keywords: Human Machine Systems
Abstract: This paper presents the results of an experiment that was performed to verify the ‘supervisory control algorithm’, a well-known model of human operator adaptation to changes in controlled element dynamics. This model proposes that human adaptive behavior is triggered once the magnitudes of the tracking error or error rate exceed certain decision region limits. In the experiment, a compensatory tracking task with a sudden transition in the controlled element dynamics, as also tested in other recent experiments, was performed by six skilled participants. In addition to performing the control task, participants had to indicate with a button press when they detected a controlled element transition. The results indicate that the published detection limits for the ‘supervisory control algorithm’ are too conservative for our experiment data, as measured detections could be related to error or error rate occurrences that exceeded 2-6 times their respective pre-transition standard deviations. The effectiveness of new detection limits proportional to these pre-transition standard deviations was tested. The best match to our experiment data was obtained with limits at 3.9 sigma, for which in only 9.38% and 11.5% of cases a (false positive) too early detection or a (false negative) missed detection occurred, respectively. Overall, these results demonstrate that human operator adaptation can indeed be effectively predicted from statistical variations in tracking error and error rate.

Keywords: Human Machine Systems
Abstract: Better understanding of manual control requires more research on human anticipatory feedforward behaviour. Recent advances include a human control model for preview tracking, and a subsystem identification (SSID) technique that uses a candidate pool approach to identify the human feedforward and feedback responses. This paper discusses the performance of the SSID method when estimating the preview control model parameters. Through simulations of a preview task with two controlled element dynamics, the SSID performance with different remnant noise levels and candidate pool densities is quantified. We demonstrate its successful application to the preview model and show that its performance deteriorates for higher noise levels. While the feedforward parameters are estimated accurately, the high-frequency compensatory feedback dynamics cannot be reliably determined. Future work focuses on alternative formulations for using SSID to estimate preview model parameters. Since in manual control the closed-loop magnitude decreases at higher frequencies, effects of manipulating the weightings of the closed-loop fitting cost values at these frequencies must be further analyzed.

Keywords: Human Machine Systems
Abstract: Mathematical human controller (HC) models are widely used in tuning manual control systems and for understanding human performance. Typically, quasi-linear HC models are used, which can accurately capture the linear portion of HCs' behavior, averaged over a long measurement window. This paper presents a deep learning HC skill-level evaluation method that works on short windows of raw HC time signals, and accounts for both the linear and non-linear portions of HC behavior. This deep learning approach is applied to data from a previous skill training experiment performed in the SIMONA Research Simulator at TU Delft. Additional human control data is generated using cybernetic HC model simulations. The results indicate that the deep learning evaluation method is successful in predicting HC skill level with 85-90% validation accuracy, but that training the classifier solely on simulated HC data reduces this accuracy by 15-25%. Inspection of the results especially shows a strong sensitivity of the classifier to the presence of remnant in the simulated training data. In conclusion, these results reveal that current quasi-linear HC model simulations, and in particular the remnant portion, do not adequately capture real time-domain HC behavior to allow effective training-data augmentation.