The application of optimal control to simulator motion cueing is examined. Existing motion cueing algorithms are hampered by the fact that they do not consider explicitly the optimal usage of simulator workspace. In this paper, numerical optimal control is used to minimize simulator platform acceleration errors, while explicitly recognizing the confines of the workspace. Actuator constraints are included and the impact of restricted actuator performance is thereby facilitated. The solution of open-loop optimal control calculations are also used as a baseline against which to compare the commonly employed linear quadratic Gaussian (LQG) and model predictive controlbased techniques. The limitations of these methods are identified and two additional modules are introduced to the LQG algorithm to improve its performance. Index Terms-Automotive engineering, vehicle driving, vehicular and wireless technologies.1063-6536

This work aims to study which level of detail should be preserved in the multibody modelling of a racing car in order to obtain reliable results without excessive model complexity. Three multibody models have been developed and compared through optimal control simulations. The models differ from each other for the order of dynamics comprised: starting from a 14 degrees of freedom (dof) car which includes chassis and wheels motion, a 10 dof model model is obtained by neglecting the wheels hop dynamics, finally a 7 dof model is derived by completely eliminating the suspension motion. Optimal control problem simulations, including parametric analyses varying the center of mass position and suspensions stiffness, have been executed on a full lap on the International circuit of Adria. Simulations results show that the 10 dof model gives almost the same results of the 14 dof one, while significantly reducing the computing time. On the contrary, the basic 7 dof model highlights remarkable differences in both the parametric analysis, suggesting the dynamics has been over-simplified.

The majority of motion cueing algorithms have been developed for passenger car applications, with correspondingly less research dedicated to race-car and high-performance vehicle simulators. In the high-performance context, the focus is on cueing the vehicle's behavioural and handling characteristics, particularly when driving near the limits of performance. In race car simulators the cueing requirements are therefore quite different, with the problem made all the more challenging by the presence of large accelerations. To understand the drivers' cueing needs, the vehicle's stability and handling response characteristics must be examined near the performance boundary. Frozen-time eigenvalue analyses are used to determine stability and response characteristics across all vehicle operating conditions, including accelerating and braking under cornering, with the results used to determine motion cueing algorithm requirements. Lateral acceleration and yaw cueing filters are designed in order to retain information critical to understanding the vehicle's behaviour on its performance boundary. Cueing filters are then tested, with the help of a professional race car driver, and are found to provide the cues necessary for the driver to control the vehicle on the limit of performance.

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