MPS: A New Method for Selecting the Stable Closed-Loop Equilibrium Attitude-Error Quaternion of a UAV During Flight
<|reference_start|>MPS: A New Method for Selecting the Stable Closed-Loop Equilibrium Attitude-Error Quaternion of a UAV During Flight: We present model predictive selection (MPS), a new method for selecting the stable closed-loop (CL) equilibrium attitude-error quaternion (AEQ) of an uncrewed aerial vehicle (UAV) during the execution of high-speed yaw maneuvers. In this approach, we minimize the cost of yawing measured with a performance figure of merit (PFM) that takes into account both the aerodynamic-torque control input and attitude-error state of the UAV. Specifically, this method uses a control law with a term whose sign is dynamically switched in real time to select, between two options, the torque associated with the lesser cost of rotation as predicted by a dynamical model of the UAV derived from first principles. This problem is relevant because the selection of the stable CL equilibrium AEQ significantly impacts the performance of a UAV during high-speed rotational flight, from both the power and control-error perspectives. To test and demonstrate the functionality and performance of the proposed method, we present data collected during one hundred real-time high-speed yaw-tracking flight experiments. These results highlight the superior capabilities of the proposed MPS-based scheme when compared to a benchmark controller commonly used in aerial robotics, as the PFM used to quantify the cost of flight is reduced by 60.30 %, on average. To our best knowledge, these are the first flight-test results that thoroughly demonstrate, evaluate, and compare the performance of a real-time controller capable of selecting the stable CL equilibrium AEQ during operation.<|reference_end|>
@article{gonçalves2024mps:,
    title={MPS: A New Method for Selecting the Stable Closed-Loop Equilibrium
  Attitude-Error Quaternion of a UAV During Flight},
    author={Francisco M. F. R. Gonc{c}alves, Ryan M. Bena, Konstantin I. Matveev,
  N'estor O. P'erez-Arancibia},
    journal={arXiv preprint arXiv:2403.07269},
    year={2024},
    doi={10.1109/ICRA57147.2024.10610463},
    archivePrefix={arXiv},
    eprint={2403.07269},
    primaryClass={cs.RO cs.SY eess.SY}
}