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Learn-to-Fly (L2F) is a new framework that aims to replace the traditionaliterative development paradigm for aerial vehicles with a combination ofreal-time aerodynamic modeling, guidance, and learning control. To ensure safelearning of the vehicle dynamics on the fly, this paper presents an$\mathcal{L}_1$ adaptive control ($\mathcal{L}_1$AC) based scheme, whichactively estimates and compensates for the discrepancy between theintermediately learned dynamics and the actual dynamics. First, to incorporatethe periodic update of the learned model within the L2F framework, this paperextends the $\mathcal{L}_1$AC architecture to handle a switched referencesystem subject to unknown time-varying parameters and disturbances. The paperalso includes an analysis of both transient and steady-state performance of the$\mathcal{L}_1$AC architecture in the presence of non-zero initialization errorfor the state predictor. Second, the paper presents how the proposed$\mathcal{L}_1$AC scheme is integrated into the L2F framework, including itsinteraction with the baseline controller and the real-time modeling module.Finally, flight tests on an unmanned aerial vehicle (UAV) validate the efficacyof the proposed control and learning scheme.

$\mathcal{L}_1$ Adaptive Control with Switched Reference Models: Application to Learn-to-Fly