Safe Learning-based Tracking Control for Quadrotors under Wind Disturbances
Safe Learning-based Tracking Control for Quadrotors under Wind Disturbances
复制标题
风扰下四旋翼飞行器安全学习跟踪控制
DOI：
10.23919/acc50511.2021.9482929
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Hui Cheng
中科院分区：
文献类型：
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作者：
Lei Zheng;Ruicong Yang;Jiesen Pan;Hui Cheng
对于受风干扰的空中机器人来说，确保精确轨迹跟踪的安全性至关重要。在本文中，我们提出了一种基于学习的安全保级级联二次规划控制（SPQC），用于风扰动下的安全轨迹跟踪。 SPQC控制器由位置级控制器和姿态级控制器组成。利用高斯过程（GP）来估计风扰动引起的不确定性，然后设计基于名义李雅普诺夫的级联二次规划（QP）控制器来跟踪参考轨迹。为了避免跟踪时出现意外障碍，以最小修改的方式在每个标称 QP 控制器上强制执行由控制障碍函数 (CBF) 表示的安全约束。通过对（a）不同风扰动下的轨迹跟踪和（b）风扰动下具有密集时变障碍场的杂乱环境中的轨迹跟踪的数值验证来说明所提出的 SPQC 控制器的性能。
Enforcing safety on precise trajectory tracking is critical for aerial robotics subject to wind disturbances. In this paper, we present a learning-based safety-preserving cascaded quadratic programming control (SPQC) for safe trajectory tracking under wind disturbances. The SPQC controller consists of a position-level controller and an attitude-level controller. Gaussian Processes (GPs) are utilized to estimate the uncertainties caused by wind disturbances, and then a nominal Lyapunov-based cascaded quadratic program (QP) controller is designed to track the reference trajectory. To avoid unexpected obstacles when tracking, safety constraints represented by control barrier functions (CBFs) are enforced on each nominal QP controller in a way of minimal modification. The performance of the proposed SPQC controller is illustrated through numerical validations of (a) trajectory tracking under different wind disturbances, and (b) trajectory tracking in a cluttered environment with a dense time-varying obstacle field under wind disturbances.