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 70% 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：同时实现复杂动态环境下四旋翼无人机的精确可靠跟踪控制极具挑战性。由于阻力与力矩变化产生的空气动力学效应具有混沌特性且难以精确辨识，当前大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"。我们提出一种新型可解释轨迹跟踪器，该框架将用于未知空气动力学效应的分布强化学习扰动估计器与随机模型预测控制器（SMPC）相集成。所提出的"约束分布强化扰动估计器"（ConsDRED）能精确辨识气动效应真实值与估计值之间的不确定性。通过采用简化仿射扰动反馈进行控制参数化以保障凸性，我们将其与SMPC相集成。理论证明ConsDRED至少达到最优全局收敛速率，且在约束违反时仍能保持特定次线性收敛速率，其误差随神经网络宽度与层数增加而递减。通过仿真与真实世界实验中的收敛训练验证其实用性，实验结果表明ConsDRED对超参数设定的敏感性低于经典约束强化学习方法。与最新技术相比，本系统将累积跟踪误差至少降低70%。重要的是，所提出的ConsDRED-SMPC框架在追求高性能与遵守保守约束之间实现了实际应用中的平衡。