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无人机同时具备精确性与可靠性的跟踪控制具有挑战性。由于源自阻力与力矩变化的气动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将其视为简单“扰动”。本文提出一种新颖且可解释的轨迹跟踪器，其集成用于未知气动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）。所提出的“约束分布强化扰动估计器”（ConsDRED）能够准确辨识气动效应真值与估计值之间的不确定性。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。我们从理论上证明：若约束违反误差随神经网络宽度与层数增加而减小，则ConsDRED至少能达到最优全局收敛速率及特定次线性速率。为验证实用性，我们在仿真与实物实验中展示了收敛训练过程，并通过实证表明ConsDRED相比经典约束强化学习方法对超参数设置更不敏感。实验证明本系统相比当前先进方法累计跟踪误差至少降低70%。重要的是，所提出的ConsDRED-SMPC框架在实际应用中平衡了追求高性能与遵守保守约束之间的权衡关系。