Model predictive control (MPC) is a promising technique for motion cueing in driving simulators, but its high computation time limits widespread real-time application. This paper proposes a hybrid algorithm that combines filter-based and MPC-based techniques to improve specific force tracking while reducing computation time. The proposed algorithm divides the reference acceleration into low-frequency and high-frequency components. The high-frequency component serves as a reference for translational motion to avoid workspace limit violations, while the low-frequency component is for tilt coordination. The total acceleration serves as a reference for combined specific force with the highest priority to enable compensation of deviations from its reference values. The algorithm uses constraints in the MPC formulation to account for workspace limits and workspace management is applied. The investigated scenarios were a step signal, a multi-sine wave and a recorded real-drive slalom maneuver. Based on the conducted simulations, the algorithm produces approximately 15% smaller root means squared error (RMSE) for the step signal compared to the state-of-the-art. Around 16% improvement is observed when the real-drive scenario is used as the simulation scenario, and for the multi-sine wave, 90% improvement is observed. At higher prediction horizons the algorithm matches the performance of a state-of-the-art MPC-based motion cueing algorithm. Finally, for all prediction horizons, the frequency-splitting algorithm produced faster results. The pre-generated references reduce the required prediction horizon and computational complexity while improving tracking performance. Hence, the proposed frequency-splitting algorithm outperforms state-of-the-art MPC-based algorithm and offers promise for real-time application in driving simulators.

翻译：模型预测控制（MPC）是驾驶模拟器中运动模拟的一项有前景技术，但其高计算时间限制了实时应用的广泛推广。本文提出一种混合算法，结合滤波与MPC技术，旨在提升比力跟踪精度并降低计算时间。该算法将参考加速度分解为低频与高频分量：高频分量作为平移运动的参考以避免工作空间边界限制，低频分量则用于倾斜协调。总加速度作为具有最高优先级的组合比力参考，以补偿参考值的偏差。算法在MPC框架中引入约束以处理工作空间限制，并采用工作空间管理策略。研究场景包括阶跃信号、多正弦波以及真实驾驶记录的蛇形机动。基于仿真结果，与现有最优方法相比，该算法在阶跃信号下的均方根误差（RMSE）降低约15%；真实驾驶场景下误差改善约16%；多正弦波场景下误差降低达90%。在较高预测时域下，该算法的性能可媲美现有最优的MPC体感算法。此外，在所有预测时域中，频段分割算法均产生更快的计算结果。预生成参考降低了所需预测时域和计算复杂度，同时提升跟踪性能。因此，本文提出的频段分割算法优于现有最优MPC算法，为驾驶模拟器的实时应用提供了可行方案。