A LQR washout algorithm for a driving simulator equipped with a hexapod platform : the relationship of neuromuscular dynamics with the sensed illness rating

Abstract

This study proposes a method and an experimental validation to analyze dynamics response of the drivers with respect to the type of the control used in the hexapod driving simulator. In this article, two different forms of motion platform tracking control have been performed: - Classical motion cueing algorithm - LQR motion cueing algorithm For each situation, the EMG (electromyography) data have been registered from arm muscles of the drivers (flexor carpi radialis, brachioradialis). In addition, the acceleration based illness ratings (IR) have been computed. In order to process the data of the EMG and IR, the linear regression with a significance level of 0.05 has been assigned. Three cases have been evaluated: 1) Time exposure neuromuscular dynamics and vestibular–vehicle level conflict illness ratings 2) Time exposure neuromuscular dynamics and vestibular level sensed illness ratings 3) Impulse dynamics effect between the neuromuscular (EMG) and the vestibular dynamics (IR) The results have showed that: a) The vibration exposure condition: When the total RMS acceleration frequency weighted average IR increases, the EMG average total power increases too by driving the classical motion cueing algorithm. However, in contrast to this, the EMG average RMS total power decreases while the IR increases during the LQR motion cueing algorithm. b) Impulse effect condition: As the IR augments; the EMG average RMS total power also increases for the optimal motion cueing algorithm but it decreases for the classical algorithm.