Carvalho, Esteban (Gipsa-Lab), Susbielle, Pierre (Grenoble-INP UGA GIPSA-Lab), Hably, Ahmad (GIPSA-lab), Dibangoye, Jilles (INSA-Lyon, Inria), Marchand, Nicolas (GIPSA-lab CNRS)

Neural Enhanced Control for Quadrotor Linear Behavior Fitting

Scheduled for presentation during the Regular Session "Multirotor Design and Control I" (WeB3), Wednesday, June 22, 2022, 17:10−17:30, Divona-1

2022 International Conference on Unmanned Aircraft Systems (ICUAS), June 21-24, 2022, Dubrovnik, Croatia

This information is tentative and subject to change. Compiled on April 26, 2024

Abstract

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.