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Batista D. Catumba
Universidade da Integração Internacional da Lusofonia Afro-Brasileira Campus das Auroras, R. José Franco de Oliveira, s/n - Zona Rural, Redenção - CE, 62790-970
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José A. Monteiro Sobrinho
Universidade da Integração Internacional da Lusofonia Afro-Brasileira Campus das Auroras, R. José Franco de Oliveira, s/n - Zona Rural, Redenção - CE, 62790-970
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Adriel de O. Freitas
Universidade da Integração Internacional da Lusofonia Afro-Brasileira Campus das Auroras, R. José Franco de Oliveira, s/n - Zona Rural, Redenção - CE, 62790-970
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Vandilberto P. Pinto
Universidade da Integração Internacional da Lusofonia Afro-Brasileira Campus das Auroras, R. José Franco de Oliveira, s/n - Zona Rural, Redenção - CE, 62790-970
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Leonardo R. Rodrigues
Divisão de Eletrônica, Instituto de Aeronáutica e Espaço - IAE, Praça Marechal Eduardo Gomes, 50, Vila das Acácias, São José dos Campos – SP, 12.228-904
Keywords:
Mathematical models, Systems identification, MATLAB, Validation, estimation
Abstract
Process modeling is a way of identifying and predicting possible failures in various systems, thus seeking to eliminate production waste. The use of mathematical models makes it possible to represent a given system in several different ways, depending on the perspective to be considered when choosing the system. This paper aims to model the data obtained from the battery discharge of a quad-rotor drone, Parrot Rolling Spider, in which two new Shoot Li-Po batteries, model XT-412, were used. For each battery, 5 tests were performed, and for each one, the battery discharge profile was recorded, ranging from 3.30V to 4.10V, at intervals of 50(mV). Such data were analyzed with the MATLAB program platform, system identification toolbox, which is used to build mathematical models of dynamic systems from measured input and output data. Within the tool, it was possible to model four mathematical models: ARX, OE, NARX and Hammerstein-Wiener, where the correctness values of these models can be seen in this work.As results it can be highlighted that the non-linear Hammerstein-Wiener model presented the highest accuracy, but presented the highest Computational Execution Time.