Investigations on Model Predictive Control Objectives for Motion Cueing Algorithms in Motorsport Driving Simulators

State of the art motion cueing algorithms aim at reproducing a simulated vehicle's motion at maximum accuracy, while respecting the motion constraints of a cueing platform. The consideration of human sensory characteristics for motion perception allows to artificially increase this envelope. Model predictive control based approaches penalize motion deviation for each perception channel and consequently minimize every error individually. However, no effort is made to balance motion cues across different degrees of freedom. In the motorsport environment it is essential to replicate vehicle characteristics precisely and consistently. The latter is of particular interest, as an inconsistent replication of cues could easily cause a perceived change of vehicle characteristics for a professional race car driver. In consequence, the motion cues should generally retain specific characteristics of the vehicle reference. A minimization of each tracking error individually does not meet this requirement which is demonstrated in this work. To overcome this limitation, two novel cost functions for a model predictive control based motion cueing algorithm are introduced which reduce the deviation of visual-vestibular incongruences. Across three degrees of freedom a reduction of up to 33 % is achieved while scaling errors and translational workspace utilization are retained at a similar level.