TY - GEN
T1 - Dynamic System Identification of Underwater Gliders based on Multi-output Gaussian Process
AU - Guo, Linyu
AU - Min, Boxv
AU - Gao, Jian
AU - Jing, Anyan
AU - Wang, Jiarun
AU - Chen, Yimin
AU - Pan, Guang
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - In this paper, a nonparametric system identification algorithm based on a multi-output Gaussian process for underwater gliders is proposed, which can predict the motion of UGs under the conditions of few training data, part measurable states, and high coupling degrees. The algorithm combines the nonlinear auto-regressive model with an external input structure and uses the conjugate gradient descent optimization algorithm to develop a nonparametric dynamic system identification scheme. The proposed scheme is implemented over data obtained from the simulated model of a UG ray-like manta of 5° and 10° Z-type steering data. The results show that the root means square errors of the prediction motion are less than 0.01500° compared with the real motion, and the multi-output Gaussian process can be accurately applied to the strong coupling, multi-degree-of-freedom (DOF) of the underwater gliders.
AB - In this paper, a nonparametric system identification algorithm based on a multi-output Gaussian process for underwater gliders is proposed, which can predict the motion of UGs under the conditions of few training data, part measurable states, and high coupling degrees. The algorithm combines the nonlinear auto-regressive model with an external input structure and uses the conjugate gradient descent optimization algorithm to develop a nonparametric dynamic system identification scheme. The proposed scheme is implemented over data obtained from the simulated model of a UG ray-like manta of 5° and 10° Z-type steering data. The results show that the root means square errors of the prediction motion are less than 0.01500° compared with the real motion, and the multi-output Gaussian process can be accurately applied to the strong coupling, multi-degree-of-freedom (DOF) of the underwater gliders.
KW - multi-output Gaussian process
KW - nonlinear auto-regressive model with external input
KW - Nonparametric system identification
KW - underwater gliders
UR - https://www.scopus.com/pages/publications/85152084211
U2 - 10.1109/USYS56283.2022.10072878
DO - 10.1109/USYS56283.2022.10072878
M3 - 会议稿件
AN - SCOPUS:85152084211
T3 - 2022 IEEE 9th International Conference on Underwater System Technology: Theory and Applications, USYS 2022
BT - 2022 IEEE 9th International Conference on Underwater System Technology
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th IEEE International Conference on Underwater System Technology: Theory and Applications, USYS 2022
Y2 - 5 December 2022 through 6 December 2022
ER -