Aerial Grasping with Soft Aerial Vehicle Using Disturbance Observer-Based Model Predictive Control Article Swipe
Hiu Ching Cheung
,
Bailun Jiang
,
Yang Hu
,
Henry K. Chu
,
Chih‐Yung Wen
,
Ching‐Wei Chang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2409.14115
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2409.14115
Aerial grasping, particularly soft aerial grasping, holds significant promise for drone delivery and harvesting tasks. However, controlling UAV dynamics during aerial grasping presents considerable challenges. The increased mass during payload grasping adversely affects thrust prediction, while unpredictable environmental disturbances further complicate control efforts. In this study, our objective aims to enhance the control of the Soft Aerial Vehicle (SAV) during aerial grasping by incorporating a disturbance observer into a Nonlinear Model Predictive Control (NMPC) SAV controller. By integrating the disturbance observer into the NMPC SAV controller, we aim to compensate for dynamic model idealization and uncertainties arising from additional payloads and unpredictable disturbances. Our approach combines a disturbance observer-based NMPC with the SAV controller, effectively minimizing tracking errors and enabling precise aerial grasping along all three axes. The proposed SAV equipped with Disturbance Observer-based Nonlinear Model Predictive Control (DOMPC) demonstrates remarkable capabilities in handling both static and non-static payloads, leading to the successful grasping of various objects. Notably, our SAV achieves an impressive payload-to-weight ratio, surpassing previous investigations in the domain of soft grasping. Using the proposed soft aerial vehicle weighing 1.002 kg, we achieve a maximum payload of 337 g by grasping.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2409.14115
- https://arxiv.org/pdf/2409.14115
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403753370
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4403753370Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2409.14115Digital Object Identifier
- Title
-
Aerial Grasping with Soft Aerial Vehicle Using Disturbance Observer-Based Model Predictive ControlWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2024Year of publication
- Publication date
-
2024-09-21Full publication date if available
- Authors
-
Hiu Ching Cheung, Bailun Jiang, Yang Hu, Henry K. Chu, Chih‐Yung Wen, Ching‐Wei ChangList of authors in order
- Landing page
-
https://arxiv.org/abs/2409.14115Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2409.14115Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2409.14115Direct OA link when available
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Disturbance (geology), Model predictive control, Control theory (sociology), Computer science, Observer (physics), Artificial intelligence, Control (management), Geography, Geology, Physics, Geomorphology, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.effectively | 114 |
| abstract_inverted_index.integrating | 77 |
| abstract_inverted_index.prediction, | 34 |
| abstract_inverted_index.significant | 7 |
| abstract_inverted_index.capabilities | 141 |
| abstract_inverted_index.considerable | 23 |
| abstract_inverted_index.demonstrates | 139 |
| abstract_inverted_index.disturbances | 38 |
| abstract_inverted_index.idealization | 93 |
| abstract_inverted_index.particularly | 2 |
| abstract_inverted_index.disturbances. | 102 |
| abstract_inverted_index.environmental | 37 |
| abstract_inverted_index.incorporating | 63 |
| abstract_inverted_index.uncertainties | 95 |
| abstract_inverted_index.unpredictable | 36, 101 |
| abstract_inverted_index.Observer-based | 133 |
| abstract_inverted_index.investigations | 167 |
| abstract_inverted_index.observer-based | 108 |
| abstract_inverted_index.payload-to-weight | 163 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile |