用扩展卡尔曼滤波提升无人机控制精度，实现高效稳定跟踪
A Class of Hierarchical Sliding Mode Control based on Extended Kalman filter for Quadrotor UAVs
- 用EKF局部线性化建模，降低噪声和干扰影响
- 三种分层滑模控制在轨迹跟踪中表现更优，计算量更低
- 理论证明稳定，实验验证方法可行
本研究提出一种新型四旋翼无人机控制方法，结合分层滑模控制（HSMC）与扩展卡尔曼滤波（EKF）。首先，通过EKF对无人机状态进行估计，利用局部线性化技术降低测量噪声和外部干扰的影响，同时减少非线性观测器的计算开销。随后，对比多种相关方法，在稳定性与计算成本方面，三种类型的HSMC——聚合式、增量式与组合式——均表现出优异的参考轨迹跟踪性能。基于李雅普诺夫稳定性理论严格分析系统稳定性。最终，实验结果与对比分析验证了所提方法的有效性与可行性。
原文摘要 · Abstract (English)
This study introduces a novel methodology for controlling Quadrotor Unmanned Aerial Vehicles, focusing on Hierarchical Sliding Mode Control strategies and an Extended Kalman Filter. Initially, an EKF is proposed to enhance robustness in estimating UAV states, thereby reducing the impact of measured noises and external disturbances. By locally linearizing UAV systems, the EKF can mitigate the disadvantages of the Kalman filter and reduce the computational cost of other nonlinear observers. Subsequently, in comparison to other related work in terms of stability and computational cost, the HSMC framework shows its outperformance in allowing the quadrotor UAVs to track the references. Three types of HSMC Aggregated HSMC, Incremental HSMC, and Combining HSMC are investigated for their effectiveness in tracking reference trajectories. Moreover, the stability of the quadrotor UAVs is rigorously analyzed using the Lyapunov stability principle. Finally, experimental results and comparative analyses demonstrate the efficacy and feasibility of the proposed methodologies.
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