オンライン学習によるデータ拡張型クアッドロータモデルと制御
Online Learning and Control for Data-Augmented Quadrotor Model
再帰的ガウス過程でクアッドロータの空気抵抗モデルをオンライン同定し、物理モデルを拡張してMPCで軌道追従精度を向上させた研究。
著者: Matej Smid, Jindrich Dunik
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
The ability to adapt to changing conditions is a key feature of a successful autonomous system. In this work, we use the Recursive Gaussian Processes (RGP) for identification of the quadrotor air drag model online, without the need of training data. The identified drag model then augments a physics-based model of the quadrotor dynamics, which allows more accurate quadrotor state prediction with increased ability to adapt to changing conditions. This data-augmented physics-based model is utilized for precise quadrotor trajectory tracking using the suitably modified Model Predictive Control (MPC) algorithm. The proposed modelling and control approach is evaluated using the Gazebo simulator and it is shown that the proposed approach tracks a desired trajectory with a higher accuracy compared to the MPC with the non-augmented (purely physics-based) model.