TY - GEN
T1 - Online Gaussian Process-Based Model Predictive Attitude Control for Underwater Gliders
AU - Guo, Linyu
AU - Min, Boxu
AU - Gao, Jian
AU - Song, Yunxuan
AU - Chen, Yimin
AU - Pan, Guang
N1 - Publisher Copyright:
© 2023 Technical Committee on Control Theory, Chinese Association of Automation.
PY - 2023
Y1 - 2023
N2 - In this paper, an online Gaussian process(GP)-based model predictive control(MPC) approach is proposed to solve the attitude control of underwater gliders(UGs) in the presence of model uncertainties. A GP model is trained online using measurement data to compensate for uncertainties of UGs including external disturbances and inner model errors. In the process of training the GP model, a genetic algorithm is used to optimize hyperparameters to minimize the difference between the model and real system. Meanwhile, a small dictionary of 500 data is designed to reduce computational burden. Simulation results show that compared with standard MPC, the proposed GP-MPC controller has better transient and steady-state performances for a UG's attitude control.
AB - In this paper, an online Gaussian process(GP)-based model predictive control(MPC) approach is proposed to solve the attitude control of underwater gliders(UGs) in the presence of model uncertainties. A GP model is trained online using measurement data to compensate for uncertainties of UGs including external disturbances and inner model errors. In the process of training the GP model, a genetic algorithm is used to optimize hyperparameters to minimize the difference between the model and real system. Meanwhile, a small dictionary of 500 data is designed to reduce computational burden. Simulation results show that compared with standard MPC, the proposed GP-MPC controller has better transient and steady-state performances for a UG's attitude control.
KW - Attitude Control
KW - Model Predictive Control
KW - Online Gaussian Process
KW - Underwater Glider
UR - http://www.scopus.com/inward/record.url?scp=85175564475&partnerID=8YFLogxK
U2 - 10.23919/CCC58697.2023.10240134
DO - 10.23919/CCC58697.2023.10240134
M3 - 会议稿件
AN - SCOPUS:85175564475
T3 - Chinese Control Conference, CCC
SP - 2771
EP - 2775
BT - 2023 42nd Chinese Control Conference, CCC 2023
PB - IEEE Computer Society
T2 - 42nd Chinese Control Conference, CCC 2023
Y2 - 24 July 2023 through 26 July 2023
ER -