Articolo in rivista, 2023, ENG, 10.1109/ACCESS.2023.3284135

Modeling nonlinear dynamics in human-machine interaction

Adriano Scibilia; Nicola Pedrocchi; Luigi Fortuna

STIIMA-CNR; University of Catania; STIIMA-CNR

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.

IEEE access Early Access , pp. 1–1

Keywords

Behavioral sciences, Mathematical models, Physiology, Nonlinear dynamical systems, Neuromuscular, Task analysis, Sensors

CNR authors

Scibilia Adriano, Pedrocchi Nicola

CNR institutes

STIIMA – Istituto di Sistemi e Tecnologie Industriali Intelligenti per il Manifatturiero Avanzato