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L1-based Model Following Control of an Identified Helicopter Model in Hover

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Picardi,  G
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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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;

/persons/resource/persons192609

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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Picardi, G., Geluardi, S., Olivari, M., & Bülthoff, H. (2016). L1-based Model Following Control of an Identified Helicopter Model in Hover. In Leveraging Emerging Technologies for Future Capabilities (pp. 1770-1777). Red Hook, NY, USA: Curran.


Cite as: http://hdl.handle.net/21.11116/0000-0000-7A9C-8
Abstract
The aim of this study is to augment the uncertain dynamics of the helicopter in order to resemble the dynamics of a new kind of vehicle, the so called Personal Aerial Vehicle. To achieve this goal a two step procedure is proposed. First, the helicopter model dynamics is augmented with a PID-based dynamic controller. Such controller implements a model following on the nominal helicopter model without uncertainties. Then, an L1 adaptive controller is designed to restore the nominal responses of the augmented helicopter when variations in the identified parameters are considered. The performance of the adaptive controller is evaluated via Montecarlo simulations. The results show that the application of the adaptive controller to the augmented helicopter dynamics can significantly reduce the effects of uncertainty due to the identification of the helicopter model. For implementation reasons the adaptive controller was applied to a subset of the outputs of the system. However, the under actuation typical of helicopters makes the tracking of the nominal responses good also on the not directly adapted outputs.