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Communication Dans Un Congrès Année : 2022

Neural Enhanced Control for Quadrotor Linear Behavior Fitting

Résumé

Designing an efficient autopilot for quadrotor can be a very long and tedious process. This comes from the complex nonlinear dynamics that rule the flying robot behavior as battery discharge, blade flapping, gyroscopic effect, frictions, etc. In this paper we propose to use a traditional cascaded control architecture enhanced with Deep Neural Network (DNN). The idea is to easily setup a control algorithm using linear cascaded laws and then correct unmodelled dynamics and approximations made during the linear control design with the DNN. The tuning process is reduced to choice of proportional and derivative gains of each control loop. The approach is tested in the ROS/Gazebo simulation environment and experimentally in a motion capture room. Results confirm that the methodology significantly improves the performance of linear approaches on nonlinear quadrotor system.
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Dates et versions

hal-03650833 , version 1 (03-05-2022)

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Esteban Carvalho, Pierre Susbielle, Ahmad Hably, Jilles Dibangoye, Nicolas Marchand. Neural Enhanced Control for Quadrotor Linear Behavior Fitting. ICUAS 2022 - IEEE International Conference on Unmanned Aircraft Systems, Jun 2022, Dubrovnik, Croatia. ⟨10.1109/ICUAS54217.2022.9836058⟩. ⟨hal-03650833⟩
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