DR-MPC: Disturbance-Resilient Model Predictive Visual Servoing Control for Quadrotor UAV Pipeline Inspect
Wen Li, Jinya Su, Cunjia Liu, Wen-Hua Chen, Shihua Li
Abstract
Unmanned Aerial Vehicles (UAVs) are gaining attention for inspections due to their improved safety, efficiency, and accuracy, alongside reduced costs and environmental risks. Visual servoing is crucial for autonomous UAV flight in GPS-degraded environments, guiding the UAV by minimizing errors between observed and desired visual features. This study focuses on Image-Based Visual Servoing (IBVS) control for quadrotor UAVs under complex dynamics and environmental disturbances. A nonlinear model predictive control (MPC) framework is first integrated with visual servoing to handle dynamics nonlinearity, control optimality, and constraints. To address uncertainties and disturbances, a Generalized Extended State Observer (GESO) is incorporated into the MPC, forming the Disturbance-Resilient (DR-) MPC. The GESO estimates the lumped disturbance to improve model predictions within the MPC horizon. The proposed algorithm is validated in a realistic Gazebo environment for UAV pipeline inspection in 3D scenarios, showing better control accuracy and reduced inspection time compared to three baseline methods: IBVS, IBVS-MPC(K) with kinematics, and IBVS-MPC(D) with dynamics. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
BibTeX
@inproceedings{iros2025_drmpcdisturbance,
title = {DR-MPC: Disturbance-Resilient Model Predictive Visual Servoing Control for Quadrotor UAV Pipeline Inspect},
author = {Wen Li and Jinya Su and Cunjia Liu and Wen-Hua Chen and Shihua Li},
booktitle = {IROS 2025},
year = {2025}
}