Closed-Loop Model Identification and MPC-based Navigation of Quadcopters: A Case Study of Parrot Bebop 2
<|reference_start|>Closed-Loop Model Identification and MPC-based Navigation of Quadcopters: A Case Study of Parrot Bebop 2: The growing potential of quadcopters in various domains, such as aerial photography, search and rescue, and infrastructure inspection, underscores the need for real-time control under strict safety and operational constraints. This challenge is compounded by the inherent nonlinear dynamics of quadcopters and the on-board computational limitations they face. This paper aims at addressing these challenges. First, this paper presents a comprehensive procedure for deriving a linear yet efficient model to describe the dynamics of quadrotors, thereby reducing complexity without compromising efficiency. Then, this paper develops a steady-state-aware Model Predictive Control (MPC) to effectively navigate quadcopters, while guaranteeing constraint satisfaction at all times. The main advantage of the steady-state-aware MPC is its low computational complexity, which makes it an appropriate choice for systems with limited computing capacity, like quadcopters. This paper considers Parrot Bebop 2 as the running example, and experimentally validates and evaluates the proposed algorithms.<|reference_end|>
@article{amiri2024closed-loop,
    title={Closed-Loop Model Identification and MPC-based Navigation of
  Quadcopters: A Case Study of Parrot Bebop 2},
    author={Mohsen Amiri, Mehdi Hosseinzadeh},
    journal={arXiv preprint arXiv:2404.07267},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.07267},
    primaryClass={cs.RO math.OC}
}