Development of an Attitude Control System of a Heavy-lift Hexacopter using Elman Recurrent Neural Networks

Kusumoputro, Benyamin and Suprijono, Herwin and Heryanto, M Ary and Suprapto, Bhakti Yudho (2016) Development of an Attitude Control System of a Heavy-lift Hexacopter using Elman Recurrent Neural Networks. 2016 22nd International Conference on Automation and Computing (ICAC). pp. 27-31.

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Abstract

Hexacopter is a type of multicopter that can be used to lift a heavy load, hence very convenient to be utilized in agricultural fields. As the consequence, however, the attitude control of this hexacopter is rather difficult compare with that of a quadcopter with four motors, due to gyroscopic effet of the additional motors and in its combination with the heavy loads. In this paper, we have developed a direct inverse controller system using an Elman neural networks for the attitude and altitude control of the hexacopter. Experiments are conducted using a flight data taken from a test-bed system. Results show that the attitude characteristics of the heavy-lift hexacopter can be controlled successfully, especially when an optimized Elman neural networks as the direct inverse controller system is utilized.

Item Type: Article
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK1-9971 Electrical engineering. Electronics. Nuclear engineering > TK1 Electrical engineering--Periodicals. Automatic control--Periodicals. Computer science--Periodicals. Information technology--Periodicals. Automatic control. Computer science. Electrical engineering. Information technology.
Divisions: Faculty of Engineering > 20201-Electrical Engineering (S1)
Depositing User: Dr. Bhakti Yudho Suprapto
Date Deposited: 03 Oct 2019 13:32
Last Modified: 03 Oct 2019 13:32
URI: http://repository.unsri.ac.id/id/eprint/9339

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