Optimizing Computational Efficiency of MPC-Based Motion Cueing Algorithm for Vehicle Driving Simulators

Vehicle driving simulators have been widely used in fields including road design, automotive development, and driver training. As a core component of the simulators, motion cueing algorithms (MCAs) aim to reproduce realistic vehicle motion sensations while respecting workspace limitations for a high-fidelity driving experience. Recent advancements have identified model predictive control (MPC) as a promising approach for MCA. However, conventional MPC-based MCA faces computational bottlenecks that limit its performance. Although recent studies have employed swarm intelligence optimization algorithms, such as the genetic algorithm and grey wolf optimizer, to enhance the performance of MPC-based MCA through horizon adjustments, the improvement path from the computational cost perspective is still overlooked. Therefore, this paper proposes a costs-optimization framework for MPC-based MCA (COMPC-based MCA), which integrates two key components: an Operator Splitting Quadratic Program (OSQP) solver and a Snow Ablation Optimizer (SAO) that considers computational costs and motion sensation errors for parameter selection. Experiment results validated the performance of the proposed framework. COMPC-based MCA can achieve fast quadratic programming (QP) solving and select appropriate parameters for performance improvement while considering computational costs.