Validation of a grip force scheduled LPV model of time-varying neuromuscular admittance

Abstract

Haptic Shared Control (HSC) systems offer a means to naturally support human drivers in the transition to automated driving. Tuning of HSC requires knowledge of the neuromuscular system (NMS) of drivers. This MSc thesis project aimed to experimentally validate a linear parameter varying (LPV) technique for modeling driver neuromuscular admittance in time-varying conditions, scheduled with grip force. LPV estimations were compared with estimates made with a Recursive Least Squares (RLS) algorithm and, where possible, a linear time-invariant (LTI) estimation technique. A human-in-the loop experiment was performed in the HMILab fixed-base driving simulator at the AE faculty. To force drivers to change their neuromuscular admittance, a steering wheel manipulation task was designed, where drivers had to keep the steering wheel angle in between limits that were visually indicated on the dashboard, while a perturbation torque was applied. Changing the limits (narrow/wide) forced drivers to adjust their admittance. The results show that the relation between admittance and grip force is not only different between subjects, but also for the same drivers in time-invariant and time-varying conditions. This leads to fundamental scheduling issues for the LPV modelling approach. The variance accounted for (VAF) for the estimated steering wheel angle varied between 0% and 90%. To overcome these scheduling problems, individual LPV models can be constructed on time-varying experiment data. RLS estimates reproduced the system output data accurately with average VAF between 80 and 90% for all experiments. RLS low-frequency (< 2.5Hz) FRF estimates closely match the LTI FRF estimates. The largest variations in driver admittance occur in this lower frequency domain, making the RLS algorithm an effective tool for identifying changes in driver neuromuscular admittance, which can be applied online.