无需模型即可在线学习，让无人机飞行更省电且计算快。
Energy-Optimal Spatial Iterative Learning within a Virtual Tube
- 基于迭代学习的无模型在线优化，不依赖精确动力学模型。
- 每轮计算复杂度仅O(n)，比传统方法快50到60倍。
- 已在多款无人机上实测，适合资源受限的实时控制场景。
由于嵌入式能源（如锂聚合物电池）续航有限，无人飞行器（UAV）的飞行时长和作业范围受到严重制约。尽管能量高效轨迹规划与控制已广泛研究，但多数方法依赖精确系统模型并需高计算成本的优化过程。本文提出一种无需模型的在线迭代学习（IL）框架，以最小化能耗。该方法无需显式建模无人机动力学或能耗特性，在保持低计算开销的同时提升能效。每轮迭代计算复杂度为O(n)，其中n为路径点数。测试结果显示，该方法相比基于模型的IPOPT基准快约50至60倍。仿真与多平台真实飞行实验验证了所提方法在有效性、计算效率和实际应用上的优势。
原文摘要 · Abstract (English)
Due to the limited endurance of embedded energy sources such as lithium-polymer (LiPo) batteries, the flight duration and operational range of unmanned aerial vehicles (UAVs) are severely constrained. Although energy-efficient trajectory planning and control have been widely studied, most existing approaches rely on accurate system models and computationally expensive optimization procedures. This paper proposes a model-free online iterative learning (IL) framework to minimize energy consumption. Without requiring explicit models of UAV dynamics or energy consumption, the proposed method improves energy efficiency while maintaining a low computational cost. The per-iteration computational complexity is O(n), where n denotes the number of path points. In the tested cases, the proposed method is approximately 50--60 times faster than the model-based IPOPT benchmark. Simulation results and real-world flight experiments across multiple UAV platforms validate the effectiveness, computational efficiency, and practical applicability of the proposed approach.
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