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.

翻译：适应环境变化的能力是成功自主系统的关键特征。本文采用递归高斯过程（RGP）在线识别四旋翼空气阻力模型，无需训练数据即可完成辨识。所辨识的阻力模型进一步增强了基于物理学的四旋翼动力学模型（physics-based model），从而在提升状态预测精度的同时增强系统对动态环境的适应能力。这种数据增强型物理模型（data-augmented physics-based model）被应用于经适当修改的模型预测控制（MPC）算法，以实现四旋翼的精确轨迹跟踪。通过Gazebo仿真平台对所提建模与控制方法进行了评估，结果表明：相较于使用非增强型（纯物理）模型的MPC，本文方法能以更高精度跟踪期望轨迹。