Adaptive Unscented Kalman Filter Based Estimation and Filtering for Dynamic Positioning with Model Uncertainties

A novel adaptive unscented Kalman filter (AUKF) is presented and applied to ship dynamic positioning (DP) system with model uncertainties of time-varying noise statistics, model mismatch and slow varying drift forces. The adaptive algorithm is proposed to simultaneously online adapt the process and...

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Bibliographic Details
Published in:International journal of control, automation, and systems 2019, Automation, and Systems, 17(3), , pp.667-678
Main Authors: Deng, Fang, Yang, Hua-Lin, Wang, Long-Jin
Format: Article
Language:English
Subjects:
Adaptive algorithms
Adaptive filters
Computer simulation
Control
Control stability
Covariance
Engineering
Kalman filters
Matching
Mechatronics
Noise
Noise measurement
Robotics
Scaling factors
Uncertainty
제어계측공학
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Summary:A novel adaptive unscented Kalman filter (AUKF) is presented and applied to ship dynamic positioning (DP) system with model uncertainties of time-varying noise statistics, model mismatch and slow varying drift forces. The adaptive algorithm is proposed to simultaneously online adapt the process and measurement noise covariance by adopting the main principle of covariance matching. The measurement noise covariance is adapted based on residual covariance matching method, and then the process noise covariance is adjusted by using adaptive scaling factor. Simulation comparisons among the proposed RQAUKF, the strong tracking UKF (RSTAUKF) and the standard UKF show that the proposed RQAUKF can effectively improve the estimation accuracy and stability, and can assist the controller to obtain better control performance.
ISSN:1598-6446
2005-4092
DOI:10.1007/s12555-018-9503-4