English(EN) Machine Learning-based Feedback Linearization Control of Quadrotor Subject to Unmodeled Dynamics

基于机器学习的控制器提升四旋翼飞行器轨迹跟踪性能

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-01 04:00

研究人员开发了一种新颖的、基于机器学习的四旋翼飞行器反馈线性化控制框架，旨在处理未建模动力学和非线性。该系统利用高斯径向基函数神经网络，实时更新其权重以补偿空气阻力和执行器动力学等不确定性。理论上保证了控制律能够确保闭环稳定性和轨迹跟踪的渐近收敛性。在Crazyflie 2.1四旋翼飞行器上的实验表明，与基线控制器相比，该方法在跟踪精度方面有了显著提高，位置范数和偏航姿态的均方根误差（RMSE）分别降低了7.13%以上和49.27%。 AI

影响这项研究可能为在复杂和不可预测环境中进行更鲁棒、更精确的无人机自主导航带来可能。

排序理由关于四旋翼飞行器新控制方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Amos Alwala, Gabriel da Silva Lima, Wallace Moreira Bessa · 2026-07-01 04:00

基于机器学习的四旋翼飞行器反馈线性化控制及其对未建模动力学的处理

arXiv:2606.31199v1 Announce Type: cross Abstract: The control of agile quadrotors in dynamic and uncertain environments remains an open area of investigation to this day, particularly when the complete system dynamics are partially known or highly nonlinear. This work introduces …

本文如何被排名

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基于机器学习的四旋翼飞行器反馈线性化控制及其对未建模动力学的处理

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