  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.
