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

• URL: http://arxiv.org/abs/2209.10007v1
• Date: Tue, 20 Sep 2022 21:30:16 GMT
• Title: Robust, High-Rate Trajectory Tracking on Insect-Scale Soft-Actuated Aerial Robots with Deep-Learned Tube MPC
• Authors: Andrea Tagliabue (1), Yi-Hsuan Hsiao (2), Urban Fasel (3), J. Nathan Kutz (4), Steven L. Brunton (5), YuFeng Chen (2) and Jonathan P. How (1) ((1) Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, (2) Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, (3) Department of Aeronautics, Imperial College London, (4) Department of Applied Mathematics, University of Washington, (5) Department of Mechanical Engineering, University of Washington)
• Abstract summary: We present an approach for agile and computationally efficient trajectory tracking on the MIT SoftFly, a sub-gram MAV (0.7 grams) Our strategy employs a cascaded control scheme, where an adaptive attitude controller is combined with a neural network policy trained to imitate a trajectory tracking robust tube model predictive controller (RTMPC) We experimentally evaluate our approach, achieving position Root Mean Square Errors lower than 1.8 cm even in the more challenging maneuvers, obtaining a 60% reduction in maximum position error compared to our previous work, and robustness demonstrating to large external disturbances.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate and agile trajectory tracking in sub-gram Micro Aerial Vehicles (MAVs) is challenging, as the small scale of the robot induces large model uncertainties, demanding robust feedback controllers, while the fast dynamics and computational constraints prevent the deployment of computationally expensive strategies. In this work, we present an approach for agile and computationally efficient trajectory tracking on the MIT SoftFly, a sub-gram MAV (0.7 grams). Our strategy employs a cascaded control scheme, where an adaptive attitude controller is combined with a neural network policy trained to imitate a trajectory tracking robust tube model predictive controller (RTMPC). The neural network policy is obtained using our recent work, which enables the policy to preserve the robustness of RTMPC, but at a fraction of its computational cost. We experimentally evaluate our approach, achieving position Root Mean Square Errors lower than 1.8 cm even in the more challenging maneuvers, obtaining a 60% reduction in maximum position error compared to our previous work, and demonstrating robustness to large external disturbances

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