TY - GEN
T1 - An Online Parameter Estimation Method Based on Adaptive Unscented Kalman Filter for Unmanned Surface Vessel
AU - Shen, Han
AU - Lv, Yuezu
AU - Zhou, Jun
AU - Wang, Linan
AU - Feng, Yuting
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - In this paper, an online parameter estimation method for unmanned surface vessels (USVs) is designed. The main idea is to establish an augmented system by viewing the parameters as system states, and then estimate the full states of the augmented system by using adaptive unscented Kalman filter (AUKF). Nine parameters including the inertial effects, the damping, the thrust allocation, and the current velocity can be online estimated accurately based on the measurements from real-time kinematic (RTK) Global Positioning System (GPS) and inertial measurement unit (IMU). The trajectory tracking control is further studied in the presence of input constraints, where the model predictive control (MPC) is introduced. The simulation results of parameter estimation demonstrate the effectiveness of the proposed method.
AB - In this paper, an online parameter estimation method for unmanned surface vessels (USVs) is designed. The main idea is to establish an augmented system by viewing the parameters as system states, and then estimate the full states of the augmented system by using adaptive unscented Kalman filter (AUKF). Nine parameters including the inertial effects, the damping, the thrust allocation, and the current velocity can be online estimated accurately based on the measurements from real-time kinematic (RTK) Global Positioning System (GPS) and inertial measurement unit (IMU). The trajectory tracking control is further studied in the presence of input constraints, where the model predictive control (MPC) is introduced. The simulation results of parameter estimation demonstrate the effectiveness of the proposed method.
KW - Adaptive unscented Kalman filter
KW - Model parameter estimation
KW - Model predictive control
KW - Unmanned surface vessel
UR - http://www.scopus.com/inward/record.url?scp=85149542740&partnerID=8YFLogxK
U2 - 10.1109/CCDC55256.2022.10033675
DO - 10.1109/CCDC55256.2022.10033675
M3 - Conference contribution
AN - SCOPUS:85149542740
T3 - Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022
SP - 2584
EP - 2589
BT - Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 34th Chinese Control and Decision Conference, CCDC 2022
Y2 - 15 August 2022 through 17 August 2022
ER -