TY - GEN
T1 - Neural networks control of hybrid-driven underwater glider
AU - Isa, Khalid
AU - Arshad, Mohd Rizal
PY - 2012
Y1 - 2012
N2 - This paper presents a neural network motion control analysis of a hybrid-driven underwater glider. The hybrid-driven underwater glider is a new breed of underwater platform, which combines the features of a conventional glider and autonomous underwater vehicle (AUV). The neural network controller based on multilayer perceptron has been designed as a predictive control. The design objective is to map the control input as well as achieving the target output. A three-layer network, which has six input nodes (control inputs), six hidden layer nodes, and fourteen output nodes is designed as the forward model architecture. Meanwhile, the inverse model of the network is used for the neural network controller. The simulation demonstrates that the control inputs of the glider motion and the target outputs of the reference model are successfully predicted and achieved. The results show that the glider is stable, and the performance of neural network controller is satisfactory, where the value of accuracy is more than 90%.
AB - This paper presents a neural network motion control analysis of a hybrid-driven underwater glider. The hybrid-driven underwater glider is a new breed of underwater platform, which combines the features of a conventional glider and autonomous underwater vehicle (AUV). The neural network controller based on multilayer perceptron has been designed as a predictive control. The design objective is to map the control input as well as achieving the target output. A three-layer network, which has six input nodes (control inputs), six hidden layer nodes, and fourteen output nodes is designed as the forward model architecture. Meanwhile, the inverse model of the network is used for the neural network controller. The simulation demonstrates that the control inputs of the glider motion and the target outputs of the reference model are successfully predicted and achieved. The results show that the glider is stable, and the performance of neural network controller is satisfactory, where the value of accuracy is more than 90%.
KW - motion
KW - neural network
KW - predictive control
KW - underwater glider
UR - http://www.scopus.com/inward/record.url?scp=84866666981&partnerID=8YFLogxK
U2 - 10.1109/OCEANS-Yeosu.2012.6263429
DO - 10.1109/OCEANS-Yeosu.2012.6263429
M3 - Conference Proceeding
AN - SCOPUS:84866666981
SN - 9781457720895
T3 - Program Book - OCEANS 2012 MTS/IEEE Yeosu: The Living Ocean and Coast - Diversity of Resources and Sustainable Activities
BT - Program Book - OCEANS 2012 MTS/IEEE Yeosu
T2 - OCEANS 2012 MTS/IEEE Yeosu Conference: The Living Ocean and Coast - Diversity of Resources and Sustainable Activities
Y2 - 21 May 2012 through 24 May 2012
ER -