无气动先验的尾座式无人机协调轨迹生成与跟踪控制

中文摘要 AI 辅助

本文提出了一种针对尾座式无人机（UAV）的协调轨迹生成与跟踪控制框架，该框架无需针对特定机身识别的气动先验，同时解决了在全飞行包线内高度非线性气动条件下的飞行控制挑战。核心创新在于针对规划与跟踪分别采用相位特定的气动建模策略，以适应其不同的功能特性，且无需机身特定的气动先验。具体而言，在协调飞行下采用phi-theory模型推导解析微分平坦映射，并建立简化但局部准确的模型用于预测控制，以实现实时气动参数估计。所提出的框架通过仿真和在微风条件下的具有挑战性的实际飞行测试进行了广泛评估，显示出在所测试的气动条件下的高精度跟踪和适应性。据我们所知，这是首次在不依赖气动识别活动的情况下，在覆盖尾座式无人机全包线的测试飞行范围内实现精确轨迹跟踪的实际演示。我们框架的源代码可在以下网址获取：此https URL。

英文摘要

This paper presents a coordinated trajectory generation and tracking control framework for a tail-sitter unmanned aerial vehicle (UAV), which does not require aerodynamic priors identified for a specific airframe while addressing the challenge of flight control under highly nonlinear aerodynamics across the full flight envelope. The core innovation lies in employing phase-specific aerodynamic modeling strategies for planning and tracking, tailored to their distinct functional characteristics, without requiring airframe-specific aerodynamic priors. Specifically, the phi-theory model under coordinated flight is employed to derive an analytic differential flatness mapping, and a simplified but locally accurate model is established for predictive control to enable real-time aerodynamic parameter estimation. The proposed framework is evaluated extensively through both simulation and challenging real-world flight tests under mild wind conditions, showing high-precision tracking and adaptability across the tested aerodynamic conditions. To the best of our knowledge, this is the first real-world demonstration of accurate trajectory tracking over tested flight regimes spanning the full envelope of a tail-sitter UAV without relying on aerodynamic identification campaigns. The source code of our framework is available at: https://github.com/SYSU-HILAB/AP-PnC.

发表机构

• Sun Yat-sen University（中山大学）
• Pengcheng Laboratory（鹏城实验室）
• Differential Robotics Technology Company, Ltd.（微分机器人科技有限公司）

机构由 AI 辅助整理，请以论文原文为准。