Modeling Nonlinear Dynamics in Human–Machine Interaction
Adriano Scibilia, Nicola Pedrocchi, Luigi Fortuna
Abstract
In Human–Machine interaction, the possibility of increasing the intelligence and adaptability of the controlled plant by imitating human control behavior has been an objective of many research efforts in the last decades. From classical control-theory human control models to modern machine learning, neural networks, and reinforcement learning paradigms, the common denominator is the effort to model complex nonlinear dynamics typical of human activity. However, these approaches are very different, and finding a guiding line is challenging. This review investigates state-of-the-art techniques from the perspective of human control modeling, considering the different physiological districts involved as the starting point. The focus is mainly directed toward nonlinear dynamical system modeling, which constitutes the main challenge in this field. In the end, transport systems are presented as a technological scenario in which the discussed techniques are mainly applied.