| dc.contributor.author | Nahom, Gebremekel | |
| dc.date.accessioned | 2022-04-11T06:18:32Z | |
| dc.date.available | 2022-04-11T06:18:32Z | |
| dc.date.issued | 2022-02-23 | |
| dc.identifier.citation | Nahom, Gebremekel. (2022).Artificial neural network design for stability control of an unmanned Aerial Vehicle (UAV). (Unpublished undergraduate dissertation) Makerere University; Kampala, Uganda. | en_US |
| dc.identifier.uri | http://hdl.handle.net/20.500.12281/11591 | |
| dc.description | A research report submitted to the school of Engineering in partial fulfillment of the requirement of the award of a degree of Science in Electrical Engineering of Makerere University. | en_US |
| dc.description.abstract | The project is about modelling an intelligent flight control system based on artificial neural network which caters for unexpected flight dynamics during UAV operations. This is to ensure UAV balanced during the flight dynamics. The control system is to address the challenge of poor performance exhibited by PID (conventional) flight control systems when faced with similar dynamics.
The artificial neural network based control system is modelled through a training process using reinforcement learning and information derived from the simulation environment. Results from this training are obtained and analysed. The performance of the PID system is also analysed. The two systems are then compared in terms of performance.
The neural network based system is seen to exhibit greater performance than the PID system in regards to stability control of a UAV. | en_US |
| dc.language.iso | en | en_US |
| dc.subject | Machine Learning | en_US |
| dc.subject | Artificial Neural Network | en_US |
| dc.subject | Reinforcement Learning | en_US |
| dc.subject | UAV | en_US |
| dc.title | Artificial neural network design for stability control of an unmanned Aerial Vehicle (UAV). | en_US |
| dc.type | Thesis | en_US |
