The Role of a Novel Discrete-Time MRAC Based Motion Cueing on Loss of Control at a Hexapod Driving Simulator

Abstract

The objective of this paper is to present the advantages of Model reference adaptive control (MRAC) motion cueing algorithm against the classical motion cueing algorithm in terms of biomechanical reactions of the participants during the critical maneuvers like chicane in driving simulator real-time. This study proposes a method and an experimental validation to analyze the vestibular and neuromuscular dynamics responses of the drivers with respect to the type of the control used at the hexapod driving simulator. For each situation, the EMG (electromyography) data were registered from arm muscles of the drivers (flexor carpi radialis, brachioradialis). In addition, the roll velocity perception thresholds (RVT) and roll velocities (RV) were computed from the real-time vestibular level measurements from the drivers via a motion-tracking sensor. In order to process the data of the EMG and RVT, Pearson's correlation and a two-way ANOVA with a significance level of 0.05 were assigned. Moreover, the relationships of arm muscle power and roll velocity with vehicle CG (center of gravity) lateral displacement were analyzed in order to assess the agility/alertness level of the drivers as well as the vehicle loss of control characteristics with a confidence interval of 95%. The results showed that the MRAC algorithm avoided the loss of adhesion, loss of control (LOA, LOC) more reasonably compared to the classical motion cueing algorithm. According to our findings, the LOA avoidance decreased the neuromuscular-visual cues level conflict with MRAC algorithm. It also revealed that the neuromuscular-vehicle dynamics conflict has influence on visuo-vestibular conflict; however, the visuo-vestibular cue conflict does not influence the neuromuscular-vehicle dynamics interactions.

Effect of Closed-Loop Motion Cueing Algorithm for a Six-Degrees-of-Freedom Dynamic Simulator on Pupil Diameter as a Driver Stress Factor

“…The motion cueing algorithm, which was a coupling of the two different algorithms (model reference adaptive control (MRAC) and linear quadratic regulator (LQR), seen in Figure 3) explained in [5,6,46,50], was included in the SCANeRstudio ® driving simulation software via dll plugin in order to accomplish the real-time driving experiments with the participations of the subjects.…”

“…These data were correlated to the 3D positions and the 3D accelerations of the vehicle CG. (electromyography) device, and human's center of pressure displacements measuring equipment Technoconcept to check postural stability [4,5,6,7,8,50]. Figure 2 illustrates the SAAM moving-base driving simulator.…”