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Frequency Domain System Identification of a Robinson R44 in Hover

MPG-Autoren
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Geluardi,  S
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Project group: Cybernetics Approach to Perception & Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Nieuwenhuizen,  FM
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Venrooij,  J
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Bülthoff,  HH
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Project group: Cybernetics Approach to Perception & Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Geluardi, S., Nieuwenhuizen, F., Venrooij, J., Pollini, L., & Bülthoff, H. (2018). Frequency Domain System Identification of a Robinson R44 in Hover. Journal of the American Helicopter Society, 63(1), 1-18. doi:10.4050/JAHS.63.012009.


Zitierlink: https://hdl.handle.net/21.11116/0000-0001-7D35-8
Zusammenfassung
The civil light helicopter domain has not fully benefited yet from the advantages system identification methods can offer. The aim of this paper is to show that system identification methods are mature enough to be successfully implemented in the civil helicopter domain. To achieve this goal, a Robinson R44 Raven II is identified in this work. The identification focuses on the hover trim condition. A lean frequency domain identification method is adopted. Furthermore, a new procedure is proposed to limit the sensitivity of the state-space minimization algorithm to initial parametric values and bounds. The resulting state-space model presents good predictive capabilities and is able to capture high-frequency rotor–body dynamics. The model is also validated with the help of a helicopter pilot by performing closed-loop control task maneuvers in the MPI CyberMotion Simulator. The overall validation shows that the implemented model is suitable for handling qualities studies, high-frequency control system designs, and realistic simulations that involve piloted closed-loop control tasks.