Reducing conflicting forces with co-adaptive haptic shared control using online time-varying operator identification

Abstract

Haptic shared control (HSC) is a method to combine the abilities of humans and machines, in which human and automation jointly exert forces on an input device. According to human-centered design, the underlying controller for HSC should closely resemble human behavior. This paper aims to continuously adapt HSC based on an online identified operator model. This approach is named coadaptive HSC. Co-adaptive HSC is hypothesized to decrease conflicts when the human adjusts his control behavior to changing requirements of the task.
In a compensatory tracking experiment with eleven participants, co-adaptive HSC is compared to time-invariant HSC. During the experiments, the participants control a time-varying controlled element, which changes from a single integrator to a double integrator and back unannounced. Time-invariant HSC was designed for controlling a single integrator, whereas co-adaptive HSC adapted to the human by continuously estimating the time-varying parameters of the operator using an Extended Kalman Filter (EKF). This online identified model of the operator was directly used in the shared controller, therefore, the co-adaptive HSC imitates human behavior.
A 36% decrease in conflict rate (the percentage of time in which the human force and the controller force have opposite directions) was found for co-adaptive HSC compared to time-invariant HSC when the controlled element was a
double integrator. However, for some participants incorrect EKF-estimation of the neuromuscular damping resulted in undesired oscillations in control force during co-adaptive HSC. This indicates that the performance of co-adaptive HSC can be further improved.
Overall, this study proves that co-adaptive HSC is a promising method to reduce conflicts, and is able to adapt to operator behavior in unforeseen situations.