MPC-based motion cueing algorithm with short prediction horizon using exponential weighting

A motion simulator is an effective tool for traininga driver in a safe environment by mimicking motion similarto the real world. To give a realistic feeling of driving andavoid motion sickness, an accurate motion cueing algorithm isrequired to restrict the platform within the allowed workspacerange while regenerating an appropriate motion feeling for thesimulator driver. Recently, employing Model Predictive Control(MPC) in the motion cueing algorithm has become popular. Inthis control method, by predicting future dynamics, an input isoptimized to minimize a cost function over a prediction horizonwhile respecting the constraints. Reducing the prediction horizonis desirable to minimize the computational burden; however itdraws the system toward instability. In this research, applying anonuniform weighting method is proposed to stabilize the motioncueing algorithm using MPC with short prediction horizon andoptimized weighting adjustment. Simulation results show theeffectiveness of the proposed method