An Online Parameter Estimation Method Based on Adaptive Unscented Kalman Filter for Unmanned Surface Vessel

Han Shen; Yuezu Lv; Jun Zhou; Linan Wang; Yuting Feng

doi:10.1109/CCDC55256.2022.10033675

An Online Parameter Estimation Method Based on Adaptive Unscented Kalman Filter for Unmanned Surface Vessel

Han Shen, Yuezu Lv^*, Jun Zhou, Linan Wang, Yuting Feng

^*Corresponding author for this work

Advanced Research Institute of Multidisciplinary Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

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.

Original language	English
Title of host publication	Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2584-2589
Number of pages	6
ISBN (Electronic)	9781665478960
DOIs	https://doi.org/10.1109/CCDC55256.2022.10033675
Publication status	Published - 2022
Event	34th Chinese Control and Decision Conference, CCDC 2022 - Hefei, China Duration: 15 Aug 2022 → 17 Aug 2022

Publication series

Name	Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022

Conference

Conference	34th Chinese Control and Decision Conference, CCDC 2022
Country/Territory	China
City	Hefei
Period	15/08/22 → 17/08/22

Keywords

Adaptive unscented Kalman filter
Model parameter estimation
Model predictive control
Unmanned surface vessel

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10.1109/CCDC55256.2022.10033675

Cite this

Shen, H., Lv, Y., Zhou, J., Wang, L., & Feng, Y. (2022). An Online Parameter Estimation Method Based on Adaptive Unscented Kalman Filter for Unmanned Surface Vessel. In Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022 (pp. 2584-2589). (Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CCDC55256.2022.10033675

Shen, Han ; Lv, Yuezu ; Zhou, Jun et al. / An Online Parameter Estimation Method Based on Adaptive Unscented Kalman Filter for Unmanned Surface Vessel. Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 2584-2589 (Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022).

@inproceedings{9210aba1ecdd494192747ac3a07e4e8f,

title = "An Online Parameter Estimation Method Based on Adaptive Unscented Kalman Filter for Unmanned Surface Vessel",

abstract = "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.",

keywords = "Adaptive unscented Kalman filter, Model parameter estimation, Model predictive control, Unmanned surface vessel",

author = "Han Shen and Yuezu Lv and Jun Zhou and Linan Wang and Yuting Feng",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 34th Chinese Control and Decision Conference, CCDC 2022 ; Conference date: 15-08-2022 Through 17-08-2022",

year = "2022",

doi = "10.1109/CCDC55256.2022.10033675",

language = "English",

series = "Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "2584--2589",

booktitle = "Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022",

address = "United States",

}

Shen, H, Lv, Y, Zhou, J, Wang, L & Feng, Y 2022, An Online Parameter Estimation Method Based on Adaptive Unscented Kalman Filter for Unmanned Surface Vessel. in Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022. Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022, Institute of Electrical and Electronics Engineers Inc., pp. 2584-2589, 34th Chinese Control and Decision Conference, CCDC 2022, Hefei, China, 15/08/22. https://doi.org/10.1109/CCDC55256.2022.10033675

An Online Parameter Estimation Method Based on Adaptive Unscented Kalman Filter for Unmanned Surface Vessel. / Shen, Han; Lv, Yuezu; Zhou, Jun et al.
Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 2584-2589 (Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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

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

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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.

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ER -

Shen H, Lv Y, Zhou J, Wang L, Feng Y. An Online Parameter Estimation Method Based on Adaptive Unscented Kalman Filter for Unmanned Surface Vessel. In Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 2584-2589. (Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022). doi: 10.1109/CCDC55256.2022.10033675

An Online Parameter Estimation Method Based on Adaptive Unscented Kalman Filter for Unmanned Surface Vessel

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