BC-NMPC：用于高速飞行的具有推进预测和重新规划的电池约束非线性模型预测控制
BC-NMPC: Battery-Constrained NMPC with Propulsion Prediction and Replanning for High-Speed Flight
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中文总结 AI 辅助
针对高速飞行中无人机因电池问题致轨迹跟踪性能下降的问题，提出将电池和推进系统模型集成到NMPC框架的方法，能实时预测相关参数，通过轨迹规划算法提高性能，实验验证了模型准确性和算法有效性。
中文摘要 AI 辅助
在高速和敏捷飞行中，由于电池耗尽和随后最大可用推力的损失，无人机（UAV）的轨迹跟踪性能会下降。在无人机竞赛等应用中，由此产生的轨迹跟踪误差会导致与障碍物碰撞并导致比赛失败。本文提出了一种将电池和推进系统模型集成到非线性模型预测控制器（NMPC）框架中的新方法，以实现对平台电压、消耗电流、功率和最大可用推力的实时预测。这使得该方法能够考虑电池放电引起的无人机最大可用推力的动态变化，从而能够为推力耗尽进行规划并提高轨迹跟踪性能。实现了一种轨迹规划算法，根据不断变化的推力限制在空中重新规划轨迹。在实际飞行实验中验证了所提出模型的准确性，同时在仿真中评估了重新规划算法的有效性。与无补偿飞行相比，我们的新方法在有障碍物的环境中实现了无碰撞飞行，跟踪均方根误差（RMSE）降低了6倍，飞行距离增加了46%，飞行时间增加了100%。
英文摘要
Trajectory tracking performance of Uncrewed Aerial Vehicles (UAVs) degrades during an agile high-speed flight due to the depletion of the battery and subsequent loss of maximum available thrust. In applications such as drone racing, this leads to a failure to complete the race due to possible collisions with obstacles. In this paper, we present a novel method for integrating battery and propulsion system models into a Nonlinear Model Predictive Controller (NMPC) framework to enable real-time prediction of the voltage, current, power, and maximum available thrust of the platform. Our proposed approach achieves lower trajectory tracking error as a result of its real-time thrust awareness, and with the help of trajectory replanning, it allows the UAV to fly in time-optimal regime throughout the mission. The accuracy of this proposed model was verified in real-world flight experiments, while the effectiveness of the replanning algorithm was evaluated in simulation. By the end of the battery capacity, compared to an unaware controller, our novel controller achieved a 25% reduction in mean position error without replanning, and an 88 % reduction with replanning.
发表机构
- Czech Technical University in Prague（捷克技术大学（布拉格））
机构由 AI 辅助整理，请以论文原文为准。
