Motion Cueing Algorithms (MCAs) encode the movement of simulated vehicles into movement that can be reproduced with a motion simulator to provide a realistic driving experience within the capabilities of the machine. This paper introduces a novel learning-based MCA for serial robot-based motion simulators. Building on the differentiable predictive control framework, the proposed method merges the advantages of Nonlinear Model Predictive Control (NMPC) - notably nonlinear constraint handling and accurate kinematic modeling - with the computational efficiency of machine learning. By shifting the computational burden to offline training, the new algorithm enables real-time operation at high control rates, thus overcoming the key challenge associated with NMPC-based motion cueing. The proposed MCA incorporates a nonlinear joint-space plant model and a policy network trained to mimic NMPC behavior while accounting for joint acceleration, velocity, and position limits. Simulation experiments across multiple motion cueing scenarios showed that the proposed algorithm performed on par with a state-of-the-art NMPC-based alternative in terms of motion cueing quality as quantified by the RMSE and correlation coefficient with respect to reference signals. However, the proposed algorithm was on average 400 times faster than the NMPC baseline. In addition, the algorithm successfully generalized to unseen operating conditions, including motion cueing scenarios on a different vehicle and real-time physics-based simulations.

翻译：运动提示算法（MCAs）将模拟载具的运动编码为可由运动模拟器复现的运动，从而在机器能力范围内提供逼真的驾驶体验。本文针对基于串联机器人的运动模拟器，提出了一种新颖的基于学习的运动提示算法。该方法基于可微分预测控制框架，融合了非线性模型预测控制（NMPC）的优势——特别是非线性约束处理与精确运动学建模——以及机器学习的计算效率。通过将计算负担转移至离线训练，新算法实现了高控制频率下的实时运行，从而克服了基于NMPC的运动提示方法面临的关键挑战。所提出的运动提示算法包含非线性关节空间被控对象模型，以及经过训练以模拟NMPC行为的策略网络，同时兼顾关节加速度、速度和位置限制。在多种运动提示场景下的仿真实验表明，就运动提示质量而言（通过相对于参考信号的均方根误差和相关系数量化），所提算法与先进的基于NMPC的替代方案表现相当。然而，所提算法平均比NMPC基准方法快400倍。此外，该算法成功泛化至未见过的运行条件，包括不同载具的运动提示场景以及基于物理的实时仿真。