Abstract
The study of human–machine interaction as a unique control system was one of the first research interests in the engineering field, with almost a century having passed since the first works appeared in this area. At the same time, it is a crucial aspect of the most recent technological developments made in application fields such as collaborative robotics and artificial intelligence. Learning the processes and dynamics underlying human control strategies when interacting with controlled elements or objects of a different nature has been the subject of research in neuroscience, aerospace, robotics, and artificial intelligence. The cross-domain nature of this field of study can cause difficulties in finding a guiding line that links motor control theory, modelling approaches in physiological control systems, and identifying human–machine general control models in manipulative tasks. The discussed models have varying levels of complexity, from the first quasi-linear model in the frequency domain to the successive optimal control model. These models include detailed descriptions of physiologic subsystems and biomechanics. The motivation behind this work is to provide a complete view of the linear models that could be easily handled both in the time domain and in the frequency domain by using a well-established methodology in the classical linear systems and control theory.
Highlights
Academic Editor: GuillermoModelling the human control action when interacting with a controlled machine has become an almost independent research field over the past few years, involving multiple disciplines and approaches
In [27], it was experimentally verified that human subjects could estimate their hand position without visual feedback and with applied external disturbances, supporting the evidence that the central nervous system internally simulates the dynamic behavior of the motor system in planning, control, and learning
All the literature’s descriptive models of neuromuscular dynamics operate at high frequencies. This consideration leads to the fact that when neuromuscular dynamics are considered as an element of a more general control model, such as the ones analyzed often only their low-frequency effects are taken into account
Summary
Modelling the human control action when interacting with a controlled machine has become an almost independent research field over the past few years, involving multiple disciplines and approaches. In [13], Hess explains this concept by giving the example of a human soldier performing a tracking task, attempting to keep a moving target within the gun’s field of view In this case, the angular error between the target and the gun’s view fielder’s azimuth can be considered the input, while the output control action is a force acting on a simple gear mechanism. This work will focus on models based on control theory, including the physiological structures involved These are part of the categories indicated in the first two rows of Table 1, which indicates the similarities between the two different indicated classification methods. The fifth and last sections will describe the most important human–machine interaction models, with the dynamics described in the previous sections being represented within a general control structure
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