Model Predictive Path Integral (MPPI) control is a widely used sampling-based approach for real-time control, valued for its flexibility in handling arbitrary dynamics and cost functions. However, it often suffers from high-frequency noise in the sampled control trajectories, which hinders the search for optimal controls and transfers to the applied controls, leading to actuator wear. In this work, we introduce Low-Pass Model Predictive Path Integral Control (LP-MPPI), which integrates low-pass filtering into the sampling process to eliminate detrimental high-frequency components and enhance the algorithm's efficiency. Unlike prior approaches, LP-MPPI provides direct and interpretable control over the frequency spectrum of sampled control trajectory perturbations, leading to more efficient sampling and smoother control. Through extensive evaluations in Gymnasium environments, simulated quadruped locomotion, and real-world F1TENTH autonomous racing, we demonstrate that LP-MPPI consistently outperforms state-of-the-art MPPI variants, achieving significant performance improvements while reducing control signal chattering.

翻译：模型预测路径积分（MPPI）控制是一种广泛使用的基于采样的实时控制方法，因其在处理任意动力学和成本函数方面的灵活性而备受重视。然而，该方法在采样控制轨迹中常存在高频噪声，这阻碍了最优控制的搜索，并传递至实际应用的控制信号，导致执行器磨损。本文提出了低通滤波模型预测路径积分控制（LP-MPPI），该方法将低通滤波集成到采样过程中，以消除有害的高频分量并提升算法效率。与先前方法不同，LP-MPPI能够对采样控制轨迹扰动的频谱进行直接且可解释的控制，从而实现更高效的采样和更平滑的控制。通过在Gymnasium环境、模拟四足机器人运动以及真实世界F1TENTH自动驾驶赛车中的广泛评估，我们证明LP-MPPI在减少控制信号抖振的同时，持续优于最先进的MPPI变体，取得了显著的性能提升。