MRAC Design for A Surveillance UAV for the Detection of Water Hyacinth
| dc.contributor.advisor | Lebsework, Negash (PhD) | |
| dc.contributor.author | Mihret, Kochito | |
| dc.date.accessioned | 2022-06-03T05:59:05Z | |
| dc.date.accessioned | 2023-11-28T14:20:39Z | |
| dc.date.available | 2022-06-03T05:59:05Z | |
| dc.date.available | 2023-11-28T14:20:39Z | |
| dc.date.issued | 2021-11 | |
| dc.description.abstract | Water hyacinth, locally named as ’Enboch’, is an invasive aquatic weed posing a great threat to the worldwide aquatic ecosystem. Its existence has been reported to greatly diminish water surfaces’ ecological value causing extensive nutrient reduction. An intuitive, but much feasible and inexpensive solution relies on the early detection of its presence followed by an action. This paper focuses on the design of a controller for a quadrotor able to perform area surveillance specifically suited for the detection of the hyacinth plant. The control design is done by taking the multivariate and non-linear nature of the problem into full consideration. The developed model reference adaptive controller (MRAC) comprising of both a standalone baseline controller and an adaptive augmentation is found to be able to stabilize the system in nominal scenarios and also restores nominal design performance in the presence of disturbances and parametric uncertainties. For the task of water hyacinth detection, the technique of transfer learning have been applied using the state-of-the-art VGG-16 model to perform feature extraction for a CNN architecture. The problem has been formulated as a multi-class classification problem considering three other aquatic plants identified as most probable on the habitats of water hyacinth. The trained model obtained an accuracy level of 93.34% through the training phase, 94.25% on a validation set, and 93% on a testing set. | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/12345678/31891 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Water hyacinth | en_US |
| dc.subject | Model reference adaptive controller | en_US |
| dc.subject | Adaptive augmentation | en_US |
| dc.subject | Minimum snap trajectory | en_US |
| dc.subject | Transfer learning | en_US |
| dc.subject | Fine tuning | en_US |
| dc.subject | Convolutional neural network | en_US |
| dc.title | MRAC Design for A Surveillance UAV for the Detection of Water Hyacinth | en_US |
| dc.type | Thesis | en_US |