面向含未知时变不确定性的四旋翼无人机的自适应反步与非奇异滑模控制
Adaptive Backstepping and Non-singular Sliding Mode Control for Quadrotor UAVs with Unknown Time-varying Uncertainties
AI总结：
针对四旋翼无人机位置姿态跟踪的奇异性与精度难题，提出级联的基于四元数的自适应反步控制与非奇异滑模控制方案，通过自适应律更新参数，保证渐近稳定性，在未知时变不确定性和大初始误差下仍具良好控制效果。
摘要：
本文提出了一种针对欠驱动垂直起降（VTOL）无人机（UAV）的新型基于四元数的非奇异控制系统。位置与姿态跟踪在奇异性和精度方面颇具挑战。本文开发了基于四元数的自适应反步控制（QABC，Quaternion-based Adaptive Backstepping Control），以级联方式解决无人机控制系统的欠驱动问题。借助QABC中设计的虚拟控制（辅助控制），生成期望姿态分量与所需推力。随后，我们提出基于四元数的滑模控制（QASMC，Quaternion-based Sliding Mode Control）以提升稳定性并缓解抖振问题，与传统滑模控制（SMC）相比，其滑模面经过修改以避免奇异性。为提升控制器鲁棒性，控制参数通过自适应律进行更新。此外，利用李雅普诺夫稳定性理论和巴尔巴莱引理，保证了平动与转动动力学的渐近稳定性。最后，本文给出了全面的对比结果，验证了所提控制器在存在未知时变参数不确定性和显著初始误差情况下的有效性。
英文摘要：
This paper presents a novel quaternion-based nonsingular control system for underactuated vertical-take-off and landing (VTOL) Unmanned Aerial Vehicles (UAVs). Position and attitude tracking is challenging regarding singularity and accuracy. Quaternion-based Adaptive Backstepping Control (QABC) is developed to tackle the underactuated issues of UAV control systems in a cascaded way. Leveraging the virtual control (auxiliary control) developed in the QABC, desired attitude components and required thrust are produced. Afterwards, we propose Quaternion-based Sliding Mode Control (QASMC) to enhance the stability and mitigate chattering issues. The sliding surface is modified to avoid singularity compared to conventional SMC. To improve the robustness of controllers, the control parameters are updated using adaptation laws. Furthermore, the asymptotic stability of translational and rotational dynamics is guaranteed by utilizing Lyapunov stability and Barbalet Lemma. Finally, the comprehensive comparison results are provided to verify the effectiveness of the proposed controllers in the presence of unknown time-varying parameter uncertainties and significant initial errors. Keywords: Non-singular Sliding Mode Control, Adaptive Backstepping Control, Unit-quaternion, Drones, Unmanned Aerial Vehicles, Asymptotic Stability, Position and Orientation Control
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
