发表机构
TU/e, Eindhoven University of Technology（埃因霍温理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对多旋翼无人机在移动平台着陆的非平衡目标问题，提出一种计算高效的鲁棒变时域MPC方法，通过限制时域搜索和利用微分平坦性实现实时控制，仿真与实验验证了其鲁棒性和有效性。
AI 中文摘要
在多移动平台上着陆多旋翼无人机（UAV）具有挑战性，因为无人机是欠驱动的，且期望的着陆状态通常是非平衡状态。收缩和变时域方法在达到此类非平衡目标方面很有前景，但通常缺乏对扰动的鲁棒性。鲁棒变时域模型预测控制（VH-MPC）解决了这一局限性，但对于像多旋翼无人机这样的高维系统来说，计算复杂度较高。本文提出了一种计算高效的鲁棒变时域MPC方法，用于在有界扰动下达到非平衡目标。在线时域搜索被限制在先前选择时域的邻域内，同时在有界扰动下保持递归可行性。一个固定的鲁棒正不变管提供了与时域无关的约束收紧。通过利用无人机的微分平坦性，该方法应用于解耦的线性化系统，从而能够在机载Raspberry Pi 5上以20 Hz的频率实时实现。仿真表明，限制时域搜索大幅减少了计算量，而对目标的影响有限，同时实时Gazebo仿真证明了该方法的鲁棒性。物理实验证明了多旋翼无人机在移动的Stewart平台上的成功着陆。
英文摘要
Landing a multirotor Unmanned Aerial Vehicle (UAV) on a moving platform is challenging because a UAV is underactuated and the desired landing state is generally a non-equilibrium state. Shrinking- and variable-horizon approaches are promising for reaching such non-equilibrium targets, but often lack robustness to disturbances. Robust Variable-Horizon Model Predictive Control (VH-MPC) addresses this limitation but is computationally complex for a high-dimensional system such as a multirotor UAV. This paper presents a computationally efficient robust Variable-Horizon MPC method for reaching non-equilibrium targets under bounded disturbances. The online horizon search is restricted to a neighborhood of the previously selected horizon, while maintaining recursive feasibility under bounded disturbances. A fixed robust positively invariant tube provides horizon-independent constraint tightening. By exploiting the differential flatness property of a UAV, this approach is applied to a decoupled linearized system, enabling real-time implementation on an onboard Raspberry Pi 5 at 20 Hz. Simulations show that restricting the horizon search substantially reduces computation with limited impact on the objective, while real-time Gazebo simulations demonstrate the robustness of the method. Physical experiments demonstrate successful landing of a multirotor UAV on a moving Stewart platform.
CommentsSubmitted to ICRA 2027
