A Model-based Motion Cueing strategy for compact driving simulation platforms

Driving simulators are widely used in different applications: driver training, vehicle development, and medical studies. To fully exploit the potential of such devices, it is crucial to develop motion control strategies that generate realistic driving feelings. This has to be achieved while keeping the platform within its limited operation space. Such strategies are called motion cueing algorithms. In this paper a particular implementation of a motion cueing algorithm is described, based on Model Predictive Control technique. A distinctive feature of such approach is that it exploits a detailed model of the human vestibular system, and consequently differs from standard motion cueing strategies based on washout filters. The algorithm has been evaluated experimentally on a small-size, innovative platform, by performing tests with professional drivers. Results show that the MPC-based motion cueing algorithm allows to effectively handle the platform working area and to devise simple and intuitive tuning procedures.