Battery Discharge forecast applied in Unmanned Aerial Vehicle
| Autor: | J. L. O. Torres, L. B. P. Nascimento, J. J. M. Sá Junior, João P. P. Gomes, Vandilberto Pereira Pinto, R. N. C. Almeida, Darielson A. Souza |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Polynomial regression
Battery (electricity) 0209 industrial biotechnology Engineering Test bench Artificial neural network business.industry Study methodology Control engineering 02 engineering and technology Kalman filter 020901 industrial engineering & automation Multilayer perceptron 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Electrical and Electronic Engineering business Extreme learning machine |
| Zdroj: | PRZEGLĄD ELEKTROTECHNICZNY. 1:187-194 |
| ISSN: | 0033-2097 |
| DOI: | 10.15199/48.2016.02.49 |
| Popis: | This paper proposes a comparative study methodology for the prediction of Li-Po (Lithium Ion Polymer) batteries discharge in UAVs (Unmanned Aerial Vehicles) using four approaches based on Artificial Neural Networks (ANNs) using Multilayer Perceptron (MLP) and Extreme Learning Machine (ELM) techniques, Polynomial Regression, and Kalman Filter (KF). The information estimates are important to assist in making decisions on which missions can be addressed to UAVs when supplied by such batteries. The data series for the experiments are obtained from tests carried out on a test bench. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|