Name
Abstract | ||
|---|---|---|
Unmanned aerial vehicles (UAV) are becoming a widely applied technology in many kinds of industries, such as agriculture and delivery transportation. However, the range of the drone is limited by the amount of energy it has left to consume. Because of this, in order to optimize the flight control, it is important to estimate the instantaneous power of the drone so that the flight controller can determine the best method to increase the operational time as well as effective energy preservation. By being able to predict this power, a drone can use such information to optimize the flight. This paper proposes the use of a neural network-based model for predicting the power consumption of a drone, which offers a prediction that is high in fidelity and adaptability. The proposed method does not require the knowledge of all the drone's characteristics, such as dynamics, which allows for easier implementation. Experiments are carried out to demonstrate the benefits of the neural network model's prediction capabilities. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.23919/ACC45564.2020.9147622 | 2020 AMERICAN CONTROL CONFERENCE (ACC) |
DocType | ISSN | Citations |
Conference | 0743-1619 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xu Ting Pamela She | 1 | 0 | 0.34 |
| Xianke Lin | 2 | 0 | 0.34 |
| Haoxiang Lang | 3 | 0 | 0.34 |