BC-NMPC: Battery-Constrained NMPC with Propulsion Prediction and Replanning for High-Speed Flight

• URL: http://arxiv.org/abs/2607.23867v1
• Date: Sun, 26 Jul 2026 22:16:36 GMT
• Title: BC-NMPC: Battery-Constrained NMPC with Propulsion Prediction and Replanning for High-Speed Flight
• Abstract summary: 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.<n>In applications such as drone racing, the trajectory tracking error leads to a collision with obstacles and a subsequent failure to complete the race.<n>We present a novel method for integrating battery and propulsion system models into a Model Predictive Controller framework to enable real-time prediction of the voltage, consumed current, power, and maximum available thrust of the platform.
• Score: 4.322810915985893
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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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