A real-time, MPC-based Motion Cueing Algorithm with Look-Ahead and driver characterization

The use of dynamic driving simulators is nowadays common practice in the automotive industry. The effectiveness of such devices is strongly related to their capabilities of well reproducing the driving sensations, hence it is crucial that the motion control strategies generate both realistic and feasible inputs to the platform. Such strategies are called Motion Cueing Algorithms (MCAs). Model Predictive Control (MPC) has been successfully applied to MCAs, being well suited to solve constrained optimal control problems. However, the predictive aspect of the algorithm has not been exploited effectively yet, mainly due to the hard real-time requirement when using a significantly long prediction window. In this paper, a real time implementation of the so called Look-Ahead (LA) strategy is presented, that is based on an effective manipulation of the reference along the prediction horizon, and on an on-line switching policy to a Non-Look-Ahead strategy when the expected driver behavior is not reliable. An optimal tuning of the MCA is computed by means of a multi-objective optimization, where both performance improvement due to the prediction exploitation, and robustness to varying driver behavior are taken into account. Finally, a characterization of the driver skill level is proposed and validated in experimental environment.