TY - GEN
T1 - Observer design for an AUV intercepting targets based on nonlinear-in-parameter neural network
AU - Guo, Xinxin
AU - Yan, Weisheng
AU - Cui, Peng
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/10/21
Y1 - 2016/10/21
N2 - To realize the system identification of an Autonomous Underwater Vehicle (AUV), a state observer based on nonlinear-in-parameter neural network is designed, which can be divided into two parts, namely, a nonlinear neural network and a conventional observer. Simulation studies are carried out, which are fixed target interception and moving target interception. The simulation results show that the presented observer can identify the AUV system states with unknown kinematics and dynamics model.
AB - To realize the system identification of an Autonomous Underwater Vehicle (AUV), a state observer based on nonlinear-in-parameter neural network is designed, which can be divided into two parts, namely, a nonlinear neural network and a conventional observer. Simulation studies are carried out, which are fixed target interception and moving target interception. The simulation results show that the presented observer can identify the AUV system states with unknown kinematics and dynamics model.
UR - http://www.scopus.com/inward/record.url?scp=84998636553&partnerID=8YFLogxK
U2 - 10.1109/ICARM.2016.7606951
DO - 10.1109/ICARM.2016.7606951
M3 - 会议稿件
AN - SCOPUS:84998636553
T3 - ICARM 2016 - 2016 International Conference on Advanced Robotics and Mechatronics
SP - 388
EP - 393
BT - ICARM 2016 - 2016 International Conference on Advanced Robotics and Mechatronics
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 International Conference on Advanced Robotics and Mechatronics, ICARM 2016
Y2 - 18 August 2016 through 20 August 2016
ER -