Estimating driver time-varying neuromuscular admittance through LPV model and grip force

Abstract

Humans can rapidly change their low-frequency arm dynamics (i.e., stiffness) to resist forces or give way to them. Quantifying driver’s time-varying arm dynamics is important for the development of steer-by-wire systems and haptic driver support systems. Conventional LTI identification, and even time-varying techniques such as wavelets, fail to capture rapidly-varying low-frequency dynamics. In this study, we propose to estimate driver admittance in real-time, using grip force measurement of the hands on the steering wheel and linear parameter-varying (LPV) modeling techniques. We hypothesized that grip force is strongly correlated to neuromuscular admittance, and can serve as an appropriate scheduling variable for an LPV model. We performed an experiment in which 18 subjects performed a boundary tracking task, and applied torque perturbations to the steering wheel to perform a baseline LTI identification. Six different boundary widths were used to evoke changes in admittance, while their grip force was measured with pressure gloves. A global LPV model is identified by linear interpolation between the local LTI models identified for each boundary width. The estimated stiffness and damping parameters varied proportionally with the grip force. Although small between-subject variations in grip force levels are found, we conclude that grip force can indeed serve as an appropriate scheduling variable for a global LPV model, which is capable of tracking fast-changing admittance changes. Future work focuses on using the LPV model in realistic driving tasks, permitting admittance estimates to be obtained without the need to apply external disturbance torques on the steering wheel.