Motion cueing algorithms for a real-time automobile driving simulator
نویسندگان
چکیده
The MCA (Motion Cueing Algorithm) for driving simulator takes into account the simulator’s workspace limits and the driver’s motion perception thresholds to reproduce simulated vehicle’s accelerations. For the motion based driving simulators, the most applied MCAs are the classical and the optimal filters. This paper presents a new algorithm, called MPC (Model Predictive Control) explicit algorithm. Compared with the filters’ algorithms, the MPC integrates directly the system constraints into its optimization process, and then gives a real optimal solution and hardly needs the tuning process to check the workspace limits and the driver’s perception thresholds. The reported MCA studies based on MPC implicit algorithm need high computational costs which can destroy the stability properties of optimal MPC in a real-time system. The proposed workspace limit condition improves significantly the MPC optimal stable condition for its application in MCA. The current MPC algorithm can achieve the complicated 2dofs optimization.
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