Corresponding author : Modified Elman Recurrent Neural Network for Attitude and Altitude Control of Heavy-lift Hexacopter

Suprapto, Bhakti Yudho (2017) Corresponding author : Modified Elman Recurrent Neural Network for Attitude and Altitude Control of Heavy-lift Hexacopter. Fakultas Teknik Universitas Indonesia - IEEE.

Text (Corresponding author QiR 2017) Corresponding author QiR2017 Modified Elman lkp.pdf Download (2MB)

Abstract

Hexacopter is a member of rotor-wing Unmanned Aerial Vehicle (UAV) which has 6 six rotors with fixed pitch blades and nonlinear characteristics that cause controlling the attitude of hexacopter is difficult. In this paper, Modified Elman Recurrent Neural Network (MERNN) is used to control attitude and altitude of Heavy-lift Hexacopter to get better performance than Elman Recurrent Neural Network (ERNN). This Modified Elman Recurrent Neural Network has a self-feedback which provides a dynamic trace of the gradients in the parameter space. In the self-feedback, the gain coefficients are trained as connection weight. This connection weight could enhance the adaptability of Elman Recurrent Neural Network to the time�varying system. The flight data are taken from a real flight experiment. Results show that the Modified Elman Recurrent Neural Network can increase performance with small error and generate a better response than Elman Recurrent Neural Network

Item Type:	Other
Subjects:	#3 Repository of Lecturer Academic Credit Systems (TPAK) > Corresponding Author
Divisions:	03-Faculty of Engineering > 20201-Electrical Engineering (S1)
Depositing User:	Mr. Bhakti Suprapto
Date Deposited:	01 May 2023 04:15
Last Modified:	01 May 2023 04:15
URI:	http://repository.unsri.ac.id/id/eprint/98676

Actions (login required)

View Item