Sensitivity Analysis of an MPC-based Motion Cueing Algorithm for a Curve Driving Scenario

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Despite gaining popularity, the use of Motion Cueing Algorithms (MCAs) based on Model Predictive Control (MPC) remains challenging due to the required tuning of a large number of cost function parameters. This paper investigates the effects of two critical MPC cost function parameters, the lateral specific force and roll rate error weights (Way and Wp), on the motion cueing quality achieved with an MPC-based MCA for a curve driving scenario. An offline sensitivity analysis, which quantified the effects of varying Way and Wp on the Root Mean Square Error (RMSE) and Pearson Correlation Coefficient (PCC) of the resulting simulator motion outputs, shows that for the same percentage-wise variation, Way has a more pronounced effect on both cueing quality predictors than Wp. In addition, for both RMSE and PCC, the effects of Way and Wp are also found to be largely independent, i.e., without interaction effects. This was further tested in a passive human-in-the-loop experiment with 20 participants and with nine different Way and Wp parameter combinations as test conditions, performed in the hexapod moving-base simulator of the Max Planck Institute for Biological Cybernetics in T¨ubingen. The collected continuous rating data, which were found to be reliable for 18/20 participants, show a statistically significant variation across all experiment conditions, and especially a strong interaction effect of Way and Wp. Somewhat surprisingly, the overall lowest continuous ratings were given to the combination of both reference weight settings from earlier research (our baseline condition). In line with the interaction effect in the continuous data, an extended post-experiment correlation analysis shows that a weighted combination of lateral specific force RMSE and and roll rate RMSE above the roll rate perception threshold strongly correlates (_ = 0.98) with the variation in mean continuous ratings across all experiment conditions. This approach can potentially be used for straightforward prediction of perceived motion cueing quality and offline MCA optimization.