@inproceedings{,
    author = {Kelm, Benjamin; Myschik, Stephan},
    title = {Model-Based Control Reconfiguration of a Multirotor UAV using Online System Identification by Sparse Regression (SINDYc)},
    editor = {},
    booktitle = {AIAA AVIATION 2023 Forum : 12-16 June 2023, San Diego, CA and Online},
    series = {},
    journal = {},
    address = {},
    publisher = {},
    edition = {},
    year = {2023},
    isbn = {},
    volume = {},
    number =  {},
    pages = {},
    url = {https://arc.aiaa.org/doi/abs/10.2514/6.2023-4464},
    doi = {10.2514/6.2023-4464},
    keywords = {},
    abstract = {Multirotor platforms are increasingly deployed for safety-critical tasks. To increase their safety and availability an automated fault handling is required. For severe faults, a mere fault accommodation with robust or adaptive control architectures is insufficient, requiring a reconfiguration of the controller structure. To make use of the explicit and analytical redundancies of the system in the reconfiguration process, an up-to-date dynamic model of the faulty system is required. In this paper we apply a sparse regression algorithm (SINDYc) to identify the system dynamics of a Redundant Multirotor Testbed (RMT), an octocopter in X8 configuration, to facilitate rapid online control reconfiguration after the sudden loss of one or multiple actuators. The control reconfiguration approach is implemented within a nonlinear simulation in MATLAB/Simulink.},
    note = {},
    institution = {Universität der Bundeswehr München, Fakultät für Maschinenbau, MB 8 - Institut für Aeronautical Engineering, Professur: Myschik, Stephan},
    
    
}