用自适应噪声建模提升无人机轨迹跟踪精度与鲁棒性
Heteroscedastic Bayesian Optimization-Based Dynamic PID Tuning for Accurate and Robust UAV Trajectory Tracking
- 引入异方差贝叶斯优化，动态调节PID参数以适应复杂环境
- 仿真与实测均显示位置误差降低24.7%~42.9%，角度误差降低40.9%~78.4%
- 适合需高精度轨迹控制的无人机系统，尤其在非平稳环境下表现优异
无人飞行器（UAV）在各类应用中至关重要，精确轨迹跟踪尤为关键。然而，传统轨迹跟踪控制算法受限于四旋翼系统的欠驱动、非线性及强耦合特性，性能不足。为此，本文提出HBO-PID，将异方差贝叶斯优化（Heteroscedastic Bayesian Optimization, HBO）框架与经典PID控制器结合，实现精准且鲁棒的轨迹跟踪。通过显式建模输入依赖的噪声方差，该方法能更好适应动态复杂环境，提升跟踪精度与鲁棒性。为加速优化收敛，采用两阶段优化策略，更高效地寻找最优控制器参数。在仿真与真实场景实验中，所提方法显著优于当前最优（SOTA）方法：位置精度提升24.7%至42.9%，角度精度提升40.9%至78.4%。
原文摘要 · Abstract (English)
Unmanned Aerial Vehicles (UAVs) play an important role in various applications, where precise trajectory tracking is crucial. However, conventional control algorithms for trajectory tracking often exhibit limited performance due to the underactuated, nonlinear, and highly coupled dynamics of quadrotor systems. To address these challenges, we propose HBO-PID, a novel control algorithm that integrates the Heteroscedastic Bayesian Optimization (HBO) framework with the classical PID controller to achieve accurate and robust trajectory tracking. By explicitly modeling input-dependent noise variance, the proposed method can better adapt to dynamic and complex environments, and therefore improve the accuracy and robustness of trajectory tracking. To accelerate the convergence of optimization, we adopt a two-stage optimization strategy that allow us to more efficiently find the optimal controller parameters. Through experiments in both simulation and real-world scenarios, we demonstrate that the proposed method significantly outperforms state-of-the-art (SOTA) methods. Compared to SOTA methods, it improves the position accuracy by 24.7% to 42.9%, and the angular accuracy by 40.9% to 78.4%.
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