用可切换模型预测控制提升飞行模拟器运动精度，尤其在剧烈机动时表现更优。
Six-DoF Stewart Platform Motion Simulator Control using Switchable Model Predictive Control
- 采用可切换模型预测控制，根据工作空间动态选择控制策略。
- 在水平失速条件下，运动追踪精度比传统方法提升42.34%以上。
- 适合高动态飞行训练场景，如紧急改出训练，对仿真真实感有显著提升。
由于六自由度（6 DoF）Stewart平台具有高刚性、灵活性和优异的强度重量比，被广泛用于构建飞行模拟器以复现飞行员训练中的运动感受。与传统的串联机械臂结构相比，复杂飞行状态下的姿态改出训练常伴随高速及角速度急剧变化。然而，基于经典洗出滤波器（CWF）的运动拟合算法（MCA）难以快速响应驱动电机，无法满足高精度要求。本文提出一种基于模型自适应架构的可切换模型预测控制（S-MPC）MCA，以缓解终端约束提取过程中的不确定性与控制解误差。验证表明，在模拟器工作包络内，基于带终端约束的MPC-MCA可实现高精度跟踪；在包络外则切换至无终端约束的MPC-MCA，获得最优跟踪解。通过水平失速条件下的平均绝对尺度（AAS）评估，所提S-MPC-MCA相比标准MPC-MCA和SWF-MCA分别提升42.34%和65.30%。
原文摘要 · Abstract (English)
Due to excellent mechanism characteristics of high rigidity, maneuverability and strength-to-weight ratio, 6 Degree-of-Freedom (DoF) Stewart structure is widely adopted to construct flight simulator platforms for replicating motion feelings during training pilots. Unlike conventional serial link manipulator based mechanisms, Upset Prevention and Recovery Training (UPRT) in complex flight status is often accompanied by large speed and violent rate of change in angular velocity of the simulator. However, Classical Washout Filter (CWF) based Motion Cueing Algorithm (MCA) shows limitations in providing rapid response to drive motors to satisfy high accuracy performance requirements. This paper aims at exploiting Model Predictive Control (MPC) based MCA which is proved to be efficient in Hexapod-based motion simulators through controlling over limited linear workspace. With respect to uncertainties and control solution errors from the extraction of Terminal Constraints (COTC), this paper proposes a Switchable Model Predictive Control (S-MPC) based MCA under model adaptive architecture to mitigate the solution uncertainties and inaccuracies. It is verified that high accurate tracking is achievable using the MPC-based MCA with COTC within the simulator operating envelope. The proposed method provides optimal tracking solutions by switching to MPC based MCA without COTC outside the operating envelope. By demonstrating the UPRT with horizontal stall conditions following Average Absolute Scale(AAS) evaluation criteria, the proposed S-MPC based MCA outperforms MPC based MCA and SWF based MCA by 42.34% and 65.30%, respectively.
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