受扰条件下多旋翼的电池感知预测轨迹规划与控制

中文摘要 AI 辅助

本文提出了一种电池感知的预测轨迹规划与控制框架，用于在空间局部扰动下运行的多旋翼。候选轨迹通过闭环车辆-电机-电池传播进行评估，从而使扰动引起的控制需求、电能、电池演变以及终端电压相关的执行器能力进入规划过程。一个降阶电池模型与独立实现的Simscape等效电路参考进行了数值基准测试，功率NRMSE为0.64%，累计能量差异低于0.7%。在包含三个扰动区域的150秒、640米任务中，所选轨迹相对于扰动感知的固定参考基线，电能消耗减少了7.46%，位置跟踪RMSE减少了约72%。规划器消融研究表明，电池相关项在标称SOC下不具约束力，但在电池耗尽压力条件下会改变所选轨迹。使用多个反馈控制器的执行进一步表明，控制器的选择改变了跟踪精度、能量消耗和执行器利用率之间的权衡。结果证明了在轨迹选择过程中考虑预测的闭环能量和电池-执行器后果的益处。

英文摘要

This paper presents a battery-aware predictive trajectory-planning and control framework for multirotors operating under spatially localized disturbances. Candidate trajectories are evaluated through closed-loop vehicle--motor--battery propagation, allowing disturbance-induced control demand, electrical energy, battery evolution, and terminal-voltage-dependent actuator capability to enter the planning process. % A reduced-order battery model is numerically benchmarked against an independently implemented Simscape equivalent-circuit reference, with a power NRMSE of $0.64\%$ and a cumulative-energy discrepancy below $0.7\%$. % In a $150$-s, $640$-m mission containing three disturbance regions, the selected trajectory reduces electrical energy consumption by $7.46\%$ and position-tracking RMSE by approximately $72\%$ relative to the disturbance-aware fixed-reference baseline. % Planner ablations show that battery-dependent terms are nonbinding at nominal SOC but alter the selected trajectory under a depleted-battery stress condition. % Execution with multiple feedback controllers further demonstrates that controller selection changes the tradeoff among tracking accuracy, energy consumption, and actuator utilization. % The results demonstrate the benefit of accounting for predicted closed-loop energetic and battery--actuator consequences during trajectory selection.

发表机构

• University of Dayton（代顿大学）

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