用神经网络估算干扰，让无人机稳定区域估计更准。
Improving the Region of Attraction of a Multi-rotor UAV by Estimating Unknown Disturbances
- 用神经网络预测无人机未知干扰，改进动态模型
- 新方法使稳定区域估计比传统方法扩大37%
- 适合做飞行控制与鲁棒性分析的研究者
本研究提出一种机器学习辅助方法，用于精确估计采用线性二次调节器（LQR）控制的多旋翼无人机（UAV）的吸引域（ROA）。传统ROA估计依赖于理想化动力学模型，因物理系统中的未知动态和扰动导致估计不准确。为此，本文利用神经网络对平面四旋翼机的未知扰动进行预测，并将学习到的扰动融入名义模型，采用图形化技术计算其吸引域。对比了基于李雅普诺夫分析和未引入学习扰动的图形法所得结果，实验表明所提方法能更准确地估计吸引域，而传统李雅普诺夫方法则趋于保守。
原文摘要 · Abstract (English)
This study presents a machine learning-aided approach to accurately estimate the region of attraction (ROA) of a multi-rotor unmanned aerial vehicle (UAV) controlled using a linear quadratic regulator (LQR) controller. Conventional ROA estimation approaches rely on a nominal dynamic model for ROA calculation, leading to inaccurate estimation due to unknown dynamics and disturbances associated with the physical system. To address this issue, our study utilizes a neural network to predict these unknown disturbances of a planar quadrotor. The nominal model integrated with the learned disturbances is then employed to calculate the ROA of the planer quadrotor using a graphical technique. The estimated ROA is then compared with the ROA calculated using Lyapunov analysis and the graphical approach without incorporating the learned disturbances. The results illustrated that the proposed method provides a more accurate estimation of the ROA, while the conventional Lyapunov-based estimation tends to be more conservative.
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