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：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。由于由阻力和力矩变化产生的气动力学具有混沌特性且难以精确辨识，当前多数四旋翼跟踪系统仅将其视为传统控制方法中的简单"扰动"。我们提出一种新型可解释轨迹跟踪器，该跟踪器集成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"约束化分布式强化扰动估计器"(ConsDRED)能够精确辨识气动效应真实值与估计值之间的不确定性。控制参数化采用简化仿射扰动反馈以保障凸性，随后将其与SMPC集成。我们从理论上证明：ConsDRED在满足约束条件时至少能达到最优全局收敛速率，而当约束条件被违反时，其误差随神经网络宽度和层数增加而递减，并可达到特定次线性收敛速率。为验证实用性，我们展示了仿真与真实世界实验中的收敛性训练，并通过实验证实ConsDRED对超参数设置的敏感性低于经典约束强化学习方法。相较于最新技术，本系统将累积跟踪误差降低至少70%。值得注意的是，所提出的ConsDRED-SMPC框架在实际应用中实现了追求高性能与遵循保守约束之间的平衡。