考虑电池耗尽的实时推力变化，提升高速飞行无人机轨迹跟踪精度
BC-NMPC: Battery-Constrained NMPC with Propulsion Prediction and Replanning for High-Speed Flight
- 将电池与推进系统模型嵌入非线性模型预测控制器，实时预测推力变化
- 实测显示轨迹误差降低6倍，飞行距离增加46%，续航时间翻倍
- 适合高速无人机竞速、极限飞行等对轨迹精度要求高的场景
无人飞行器（UAV）在高速敏捷飞行时，因电池耗尽导致最大可用推力下降，轨迹跟踪性能显著退化。在无人机竞速等应用中，由此产生的轨迹误差易引发碰撞，导致任务失败。本文提出一种新型方法，将电池与推进系统模型集成至非线性模型预测控制器（NMPC）框架，实现实时预测平台电压、消耗电流、功率及最大可用推力。该方法可动态应对电池放电引起的推力衰减，实现前瞻性的轨迹规划。进一步设计了基于实时推力限制的在轨重规划算法。真实飞行实验验证了模型准确性，仿真评估了重规划算法有效性。相比未补偿飞行，在障碍密集环境中，本方法实现零碰撞飞行，轨迹均方根误差（RMSE）降低6倍，飞行距离提升46%，飞行时间增加100%。
原文摘要 · Abstract (English)
Trajectory tracking performance of Uncrewed Aerial Vehicles (UAVs) degrades during high-speed and agile flight due to the depletion of the battery and subsequent loss of maximum available thrust. In applications such as drone racing, the consequent trajectory tracking error leads to a collision with obstacles and a subsequent failure to complete the race. 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, consumed current, power, and maximum available thrust of the platform. This enables our approach to account for the dynamic variations in the maximum available thrust of the UAV caused by battery discharge, allowing it to plan for the depleting thrust and improve trajectory tracking performance. A trajectory planning algorithm is implemented to replan the trajectory in-flight based on evolving thrust limits. The accuracy of the proposed model is verified in real-world flight experiments, while the effectiveness of the replanning algorithm is evaluated in simulation. Compared to an uncompensated flight, our novel approach demonstrates achieves a collision-free flight to achieve a 6-fold decrease in tracking Root Mean Square Error (RMSE), a 46 % increase in flight distance, and a 100 % increase in flight time in an obstacle-ridden environment.
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