A novel Motion Cueing Algorithm based on real-time optimization and periodic invariant sets

This paper deals with control design of Motion Cueing Algorithms for driving simulation. The development of driving-assistance systems and the gradual move towards autonomous driving led automobile manufacturers to focus on high performance driving simulation in order to validate novel functionalities and driving confort before production. Driving simulators are currently constrained environments because of the workspace size and the actuators resistance. As part of the software operating the platform, a control block has to manage the position of a cabin by guaranteeing realistic acceleration feelings to a driver. In this purpose, the controller is usually design within Model Predictive Control (MPC) framework. However large prediction horizons and constrained tracking problems implies heavy computational burden. In this paper, a novel MPC-based motion cueing algorithm is proposed considering periodic invariant sets as a key concept to decrease the complexity of the real-time optimization.

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