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As robots and other automated systems are introduced to unknown and dynamicenvironments, robust and adaptive control strategies are required to cope withdisturbances, unmodeled dynamics and parametric uncertainties. In this paper,we propose and provide theoretical proofs of a combined $\mathcal{L}_1$adaptive feedback and iterative learning control (ILC) framework to improvetrajectory tracking of a system subject to unknown and changing disturbances.The $\mathcal{L}_1$ adaptive controller forces the system to behave in arepeatable, predefined way, even in the presence of unknown and changingdisturbances; however, this does not imply that perfect trajectory tracking isachieved. ILC improves the tracking performance based on experience fromprevious executions. The performance of ILC is limited by the robustness andrepeatability of the underlying system, which, in this approach, is handled bythe $\mathcal{L}_1$ adaptive controller. In particular, we are able togeneralize learned trajectories across different system configurations becausethe $\mathcal{L}_1$ adaptive controller handles the underlying changes in thesystem. We demonstrate the improved trajectory tracking performance andgeneralization capabilities of the combined method compared to pure ILC inexperiments with a quadrotor subject to unknown, dynamic disturbances. This isthe first work to show $\mathcal{L}_1$ adaptive control combined with ILC inexperiment.

High-Precision Trajectory Tracking in Changing Environments Through $\mathcal{L}_1$ Adaptive Feedback and Iterative Learning