@inproceedings{8fbfa76413224d07aace6bb9b1468501,
title = "Robust MPC-based Trajectory Tracking Control for Quadrotor UAV-slung Load System",
abstract = "This article investigates the problem of trajectory tracking control and avoiding collisions for a disturbed quadrotor unmanned aerial vehicle (UAV)-slung load system, specifically focusing on the scenario where the reference trajectory is unreachable. A tube-based model predictive controller (MPC) is presented, which enables simultaneous control of the quadrotor UAV's position and the payload's swing angles. Additionally, the introduced controller can also suppress the disturbances caused by the swing of the rope and the load. To ensure collision avoidance with both dynamic and static obstacles, MINVO basis is employed to calculate the minimum volume of the exterior polyhedral approximation of the obstacles' paths. The challenge of tracking an unreachable reference trajectory is effectively addressed through the integration of a trajectory planner and a trajectory tracking controller within a unified tube-based MPC problem. Detailed simulation results illustrate the efficacy of the introduced controller in a limited space with obstacles.",
keywords = "Collision Avoidance, Quadrotor UAV-slung Load System, Trajectory Tracking, Tube-based MPC",
author = "Chenlong Fu and Haidi Sun and Li Dai and Yuanqing Xia",
note = "Publisher Copyright: {\textcopyright} 2024 Technical Committee on Control Theory, Chinese Association of Automation.; 43rd Chinese Control Conference, CCC 2024 ; Conference date: 28-07-2024 Through 31-07-2024",
year = "2024",
doi = "10.23919/CCC63176.2024.10661941",
language = "English",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "2826--2833",
editor = "Jing Na and Jian Sun",
booktitle = "Proceedings of the 43rd Chinese Control Conference, CCC 2024",
address = "United States",
}