Multi-Objective LQR-Based Optimization of Motion Cueing for Redundant Ski Simulator
Résumé
This paper presents an innovative multi-objective optimization system which optimizes Motion Cueing Algorithm (MCA) parameters for an eight-degree of-freedom (8-DoF) ski simulator that enhances rehabilitation and virtual sports training for disabled individuals. The system unites human vestibular models with Linear Quadratic Regulator (LQR)-based optimal control to achieve minimum perceptual differences between actual and simulated movements under the simulator’s mechanical limitation conditions. The system uses redundancy resolution to distribute motion between the 6-DoF hexapod and XY-table subsystem which enhances both workspace efficiency and motion precision. The proposed method demonstrates superior performance through simulation results which show it achieves 97% perceptual accuracy better than traditional washout filters. The system enables VR-based training and rehabilitation because it provides precise human sensation and redundant actuation capabilities.