Simultaneously accurate and reliable tracking control for quadrotors in complex dynamic environments is challenging. As aerodynamics derived from drag forces and moment variations are chaotic and difficult to precisely identify, most current quadrotor tracking systems treat them as simple `disturbances' in conventional control approaches. We propose a novel, interpretable trajectory tracker integrating a Distributional Reinforcement Learning disturbance estimator for unknown aerodynamic effects with a Stochastic Model Predictive Controller (SMPC). The proposed estimator `Constrained Distributional Reinforced disturbance estimator' (ConsDRED) accurately identifies uncertainties between true and estimated values of aerodynamic effects. Simplified Affine Disturbance Feedback is used for control parameterization to guarantee convexity, which we then integrate with a SMPC. We theoretically guarantee that ConsDRED achieves at least an optimal global convergence rate and a certain sublinear rate if constraints are violated with an error decreases as the width and the layer of neural network increase. To demonstrate practicality, we show convergent training in simulation and real-world experiments, and empirically verify that ConsDRED is less sensitive to hyperparameter settings compared with canonical constrained RL approaches. We demonstrate our system improves accumulative tracking errors by at least 62% compared with the recent art. Importantly, the proposed framework, ConsDRED-SMPC, balances the tradeoff between pursuing high performance and obeying conservative constraints for practical implementations

翻译：在复杂动态环境中实现四旋翼精确且可靠的跟踪控制极具挑战性。由于气动阻力与力矩变化衍生的空气动力学效应具有混沌特性且难以精确辨识，当前多数四旋翼跟踪系统在传统控制方法中仅将其视为简单"扰动"。本文提出一种创新的可解释轨迹跟踪器，该跟踪器融合了用于未知气动效应的分布式强化学习扰动估计器与随机模型预测控制器（SMPC）。所提出的"带约束的分布式强化扰动估计器"（ConsDRED）能够准确识别气动效应真实值与估计值之间的不确定性。我们采用简化仿射扰动反馈进行控制参数化以保证凸性，进而将其与SMPC集成。理论证明，ConsDRED可获得至少最优全局收敛速率，且在约束违反情况下可实现特定次线性收敛，其误差随神经网络宽度和层数增加而递减。为验证实用性，我们展示了仿真与真实世界实验中的收敛训练过程，并通过实验证明与经典约束强化学习方法相比，ConsDRED对超参数设置的敏感性更低。实验表明，与最新技术相比，本系统累计跟踪误差至少降低62%。重要的是，所提出的ConsDRED-SMPC框架能在实际应用中平衡追求高性能与遵守保守约束之间的权衡关系。