TY - GEN
T1 - Elman Recurrent Neural Networks Based Direct Inverse Control for Quadrotor Attitude and Altitude Control
AU - Kamanditya, Bharindra
AU - Kusumoputro, Benyamin
N1 - Funding Information:
Acknowledgment This work is supported by the Ministry of Research and Technology through DRPM Universitas Indonesia for the fiscal year of 2020.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/6
Y1 - 2020/6
N2 - The controller system for quadrotor is a challenging research topic due to various application purposes. The usually used controller for quadrotor is developed based on a PID method due to its low cost, simple structure and easy to design with an acceptable error. However, determining precisely the PID's parameters is very difficult, especially when the characteristics of the plant are highly nonlinear, underactuated and cross-coupling. In this paper, a neural networks-based controller system through Elman recurrent learning mechanism is then proposed. Using a real-time flight dataset from the quadrotor, neural networks based direct inverse control scheme of attitude and altitude control system is constructed and tested through simulation. Experimental results show that the Elman neural networks-based controller system works within very low MSSE when it is used to follow the reference flight testing dataset. Experiments also confirmed that the Elman recurrent neural network shows better performance characteristics compared with that of the Backpropagation neural network.
AB - The controller system for quadrotor is a challenging research topic due to various application purposes. The usually used controller for quadrotor is developed based on a PID method due to its low cost, simple structure and easy to design with an acceptable error. However, determining precisely the PID's parameters is very difficult, especially when the characteristics of the plant are highly nonlinear, underactuated and cross-coupling. In this paper, a neural networks-based controller system through Elman recurrent learning mechanism is then proposed. Using a real-time flight dataset from the quadrotor, neural networks based direct inverse control scheme of attitude and altitude control system is constructed and tested through simulation. Experimental results show that the Elman neural networks-based controller system works within very low MSSE when it is used to follow the reference flight testing dataset. Experiments also confirmed that the Elman recurrent neural network shows better performance characteristics compared with that of the Backpropagation neural network.
KW - artificial neural networks control
KW - direct inverse control scheme
KW - Elman recurrent neural networks
KW - quadrotor
UR - http://www.scopus.com/inward/record.url?scp=85090852974&partnerID=8YFLogxK
U2 - 10.1109/ICIEM48762.2020.9160191
DO - 10.1109/ICIEM48762.2020.9160191
M3 - Conference contribution
AN - SCOPUS:85090852974
T3 - Proceedings of International Conference on Intelligent Engineering and Management, ICIEM 2020
SP - 39
EP - 43
BT - Proceedings of International Conference on Intelligent Engineering and Management, ICIEM 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 International Conference on Intelligent Engineering and Management, ICIEM 2020
Y2 - 17 June 2020 through 19 June 2020
ER -