论文标题
Quadue-CCM：使用不确定的收缩指标进行精确四轨迹跟踪的不确定收缩指标的可解释分配加固学习
QuaDUE-CCM: Interpretable Distributional Reinforcement Learning using Uncertain Contraction Metrics for Precise Quadrotor Trajectory Tracking
论文作者
论文摘要
准确性和稳定性是四轨轨迹跟踪系统的常见要求。设计准确，稳定的跟踪控制器仍然具有挑战性，尤其是在具有复杂空气动力学干扰的未知和动态环境中。我们提出了基于分位数的分布分布 - 增强不确定性估计量（quadue），以准确识别空气动力学障碍的影响，即真实和估计的控制收缩度量（CCM）之间的不确定性。从收缩理论中汲取灵感并整合了不确定性的Quadue，我们的新型基于CCM的轨迹跟踪框架可以准确地跟踪任何可行的参考轨迹，同时保证指数融合。更重要的是，分别从理论角度保证和分析了分布RL的收敛和训练加速度。我们还在未知和多样化的空气动力下演示了我们的系统。在大型空气动力（> 2m/s^2）下，与经典数据驱动方法相比，我们的Quadue-CCM在跟踪误差方面至少提高了56.6％。与四型MPC（一种基于分布RL的方法）相比，Quadue-CCM的收缩率至少提高了3倍。
Accuracy and stability are common requirements for Quadrotor trajectory tracking systems. Designing an accurate and stable tracking controller remains challenging, particularly in unknown and dynamic environments with complex aerodynamic disturbances. We propose a Quantile-approximation-based Distributional-reinforced Uncertainty Estimator (QuaDUE) to accurately identify the effects of aerodynamic disturbances, i.e., the uncertainties between the true and estimated Control Contraction Metrics (CCMs). Taking inspiration from contraction theory and integrating the QuaDUE for uncertainties, our novel CCM-based trajectory tracking framework tracks any feasible reference trajectory precisely whilst guaranteeing exponential convergence. More importantly, the convergence and training acceleration of the distributional RL are guaranteed and analyzed, respectively, from theoretical perspectives. We also demonstrate our system under unknown and diverse aerodynamic forces. Under large aerodynamic forces (>2m/s^2), compared with the classic data-driven approach, our QuaDUE-CCM achieves at least a 56.6% improvement in tracking error. Compared with QuaDRED-MPC, a distributional RL-based approach, QuaDUE-CCM achieves at least a 3 times improvement in contraction rate.
