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- W4312397851 abstract "Flexible, deforming aerial vehicles have introduced a paradigm shift in existing research by enabling stable aerial-physical interactions. These vehicles harvest interaction energies to undergo a deformation of the vehicle body and absorb impacts, therefore retaining stability under the action of unknown external forces. The low-level control of such aerial vehicles gets complicated owing to the highly nonlinear and varying dynamics of a passive deforming chassis, coupled with the action of external forces resulting from interactions with the environments. In this work, we propose a morphology-aware Q-learning-based tracking controller that accounts for varying morphology and the various external interaction forces. We train this learning-based controller offline under various case scenarios and then augment an online rollout scheme to improve the tracking performance in case of model-mismatch. Simulation studies are presented that validate the proposed controller in comparison with a conventional disturbance observer approach with improved tracking performance and optimized control effort." @default.
- W4312397851 created "2023-01-04" @default.
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- W4312397851 date "2022-01-01" @default.
- W4312397851 modified "2023-09-25" @default.
- W4312397851 title "Learning-based Control for Passive Flexible Multirotors (PFMs) with Physical Interaction Abilities" @default.
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- W4312397851 doi "https://doi.org/10.1016/j.ifacol.2022.11.198" @default.
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