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Transforming Civil Helicopters into Personal Aerial Vehicles: Modeling, Control, and Validation

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

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Olivari,  M
Max Planck Institute for Biological Cybernetics, Max Planck Society;
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;
Project group: Motion Perception & Simulation, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Bülthoff,  HH
Project group: Cybernetics Approach to Perception & Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
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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Zitation

Geluardi, S., Venrooij, J., Olivari, M., Bülthoff, H., & Pollini, L. (2017). Transforming Civil Helicopters into Personal Aerial Vehicles: Modeling, Control, and Validation. Journal of Guidance, Control, and Dynamics, 40(10), 2481-2495. doi:10.2514/1.G002605.


Zitierlink: http://hdl.handle.net/21.11116/0000-0000-C2AA-5
Zusammenfassung
This paper presents the implementation of robust control strategies to augment an identified state-space model of a civil light helicopter. The aim of this study is to augment the helicopter model to achieve response types and handling qualities of a new category of aircraft called personal aerial vehicles, which can be flown even by inexperienced pilots. Two control methods are considered to augment the helicopter model, H∞ and μ synthesis. Differences, advantages, and limitations of the implemented control architectures are highlighted with respect to the personal aerial vehicle reference dynamics in terms of robust stability, nominal performance, and handling qualities. Furthermore, results are presented of an experiment performed with the Max Planck Institute CyberMotion Simulator. The aim of the experiment is to assess the discrepancies between the two augmented systems and the personal aerial vehicle reference model. The experiment consists of piloted closed-loop control tasks performed by participants without any prior flight experience. The results show that the two implemented augmented systems allow inexperienced pilots to achieve workload and performance levels comparable to those defined for personal aerial vehicles.