Tuesday, February 17, 2015, 15:00 - 16:00
Room 01-012, Georges-Köhler-Allee 102, Freiburg 79110, Germany
A motion simulator is a device designed to provide the human subject with a realistic feeling of being inside a moving vehicle, such as a car or an airplane. Humans perceive self-motion mainly via visual and vestibular systems. While current computer graphics technology can provide very realistic visual cues, realism of inertial cues is strictly limited by physical constraints of the motion system, such as maximum positions, velocities and accelerations of the mechanical axes.
The challenge is to control the motion system is such a way that the difference between its output (accelerations and rotational velocities) and the corresponding values in a real vehicle is minimized, while satisfying the motion system constraints.
We solve this problem by using a large-scale optimization algorithm to find motion system input for a pre-defined reference output. By using the CasADi framework, we managed to take second-order derivatives into account, which has greatly improved the convergence and decreased the computation time. We tested our approach on a Max-Planck Institute Cyber Motion Simulator.
Our final goal is to make the algorithm work in a real-time scenario, where a human subject is actively controlling the simulated vehicle. We want to implement a non-linear model-predictive controller that would be fast enough for this purpose.