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

提出了一种新型的轨迹跟踪器，其集成了分布式强化学习扰动估计器和随机模型预测控制器，用于准确识别不确定的气动效应，并优化控制参数来保证凸性，实现了至少最优全局收敛速率和一个确定的亚线性速率，对于实际应用提出的 ConsDRED-SMPC 框架在在模拟和实际实验中均得到了验证和评估

四旋翼无人机跟踪控制系统的可信强化学习

Trustworthy Reinforcement Learning for Quadrotor UAV Tracking Control Systems

Aerodynamic forces render accurate high-speed trajectory tracking with
quadrotors extremely challenging. These complex aerodynamic effects become a
significant disturbance at high speeds, introducing large positional tracking
errors, and are extremely difficult to model. To fly at high speeds, feedback
control must be able to account for these aerodynamic effects in real-time.
This necessitates a modelling procedure that is both accurate and efficient to
evaluate. Therefore, we present an approach to model aerodynamic effects using
Gaussian Processes, which we incorporate into a Model Predictive Controller to
achieve efficient and precise real-time feedback control, leading to up to 70%
reduction in trajectory tracking error at high speeds. We verify our method by
extensive comparison to a state-of-the-art linear drag model in synthetic and
real-world experiments at speeds of up to 14m/s and accelerations beyond 4g.

采用高斯过程模型建模空气动力学特性，并将其并入模型预测控制器，从而实现高速无人机精确控制。