  This paper provides a preliminary study for an efficient learning algorithm
by reasoning the error from first principle physics to generate learning
signals in near real time. Motivated by iterative learning control (ILC), this
learning algorithm is applied to the feedforward control loop of the unmanned
aerial vehicles (UAVs), enabling the learning from errors made by other UAVs
with different dynamics or flying in different scenarios. This learning
framework improves the data utilization efficiency and learning reliability via
analytically incorporating the physical model mapping, and enhances the
flexibility of the model-based methodology with equipping it with the
self-learning capability. Numerical studies are performed to validate the
proposed learning algorithm.
