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Accurate and agile trajectory tracking in sub-gram Micro Aerial Vehicles(MAVs) is challenging, as the small scale of the robot induces large modeluncertainties, demanding robust feedback controllers, while the fast dynamicsand computational constraints prevent the deployment of computationallyexpensive strategies. In this work, we present an approach for agile andcomputationally efficient trajectory tracking on the MIT SoftFly, a sub-gramMAV (0.7 grams). Our strategy employs a cascaded control scheme, where anadaptive attitude controller is combined with a neural network policy trainedto imitate a trajectory tracking robust tube model predictive controller(RTMPC). The neural network policy is obtained using our recent work, whichenables the policy to preserve the robustness of RTMPC, but at a fraction ofits computational cost. We experimentally evaluate our approach, achievingposition Root Mean Square Errors lower than 1.8 cm even in the more challengingmaneuvers, obtaining a 60% reduction in maximum position error compared to ourprevious work, and demonstrating robustness to large external disturbances

Robust, High-Rate Trajectory Tracking on Insect-Scale Soft-Actuated Aerial Robots with Deep-Learned Tube MPC