发表机构
Czech Technical University in Prague（布拉格捷克理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出L1-MPPI，级联L1自适应控制与MPPI，增强动态模型，提升高速无人机在不确定性和扰动下的跟踪精度，仿真与实测均显著降低RMSE。
AI 中文摘要
这项工作提出了L1自适应模型预测路径积分（L1-MPPI）。它将L1自适应控制与模型预测路径积分（MPPI）级联，以提高高速无人机轨迹的跟踪精度。得益于L1增强，即使在模型不确定性和外部扰动（如额外载荷或建模的空气动力阻力不匹配）下，跟踪仍然保持准确。与现有的未显式建模空气动力效应、变化载荷且通常忽略底层电机控制器动态的无人机控制MPPI方法相比，我们的L1-MPPI方法通过纳入底层飞行控制器和电机动态，以及反映底层控制器方法的迭代混合方案，增强了MPPI中使用的动态模型。所提出的方法在仿真和现实世界中均展示了改进的跟踪性能，即使无人机承受未知载荷也是如此。在质量增加35%的飞行中，我们的方法相对于普通MPPI将RMSE降低了58.61%。与使用在线质量估计器代替L1增强的相同MPPI相比，RMSE降低了38.59%。在现实世界实验中，无人机达到了高达13.50米/秒的速度和高达2.5g的加速度。
英文摘要
This work proposes the L1 Adaptive Model Predictive Path Integral (L1-MPPI). It cascades L1 adaptive control with the Model Predictive Path Integral (MPPI) to improve tracking of high-speed UAV trajectories. Thanks to the L1augmentation, the tracking remains accurate even under model uncertainties and external disturbances, such as an additional payload or a mismatch in the modeled aerodynamic drag. In contrast to existing MPPI approaches for UAV control that do not explicitly model aerodynamic effects, varying payloads, and typically neglect the dynamics of low-level motor controllers, our L1-MPPI approach enhances the dynamic model used in the MPPI by incorporating the low-level flight controller and motor dynamics, as well as an iterative mixing scheme that reflects the approach of the low-level controller. The proposed method demonstrates improved tracking performance in both simulation and the real world, even when the UAV is subjected to an unknown payload. In flight with 35% mass increase, our approach lowers the RMSE by 58.61% with respect to plain MPPI. Compared to the same MPPI using an online mass estimator in place of the L1 augmentation, the RMSE is lower by 38.59%. During the real-world experiments the UAV reaches speeds up to 13.50 m/s and accelerations up to 2.5 g.
