A1 Journal article (refereed), original research

Adaptive Square-root Unscented Kalman Filter: An experimental study of hydraulic actuator state estimation

Open Access publication

Publication Details
Authors: Mohammadi Asl Reza, Shabbouei Hagh Yashar, Simani Silvio, Handroos Heikki
Publisher: Elsevier
Publication year: 2019
Language: English
Related Journal or Series Information: Mechanical Systems and Signal Processing
Volume number: 132
Start page: 670
End page: 691
Number of pages: 22
ISSN: 0888-3270
eISSN: 1096-1216
JUFO-Level of this publication: 3
Open Access: Open Access publication
Location of the parallel saved publication: http://urn.fi/URN:NBN:fi-fe2019080823777


This paper introduces a new adaptive Kalman filter for nonlinear systems. The proposed method is an adaptive version of the square-root unscented Kalman filter (Sr-UKF). The presented adaptive square-root unscented Kalman filter (ASr-UKF) is developed to estimate/detect the states of a nonlinear system while noise statistics that affect system measurement and states are unknown. The filter attempts to adaptively estimate means and covariances of both process and measurement noises and also the states of the system simultaneously. This evaluation of the value of covariances helps the filter to modify itself in order to have more precise estimation. To test the efficiency of the investigated filter, it is applied to different approaches, including state estimation and fault detection. First, the proposed filter is used to predict states of two different nonlinear systems: a robot manipulator and a servo-hydraulic system. Second, the filter is employed to detect a leakage fault in a hydraulic system. All applications are tested under three assumptions: noises with known constant statistics, noises with unknown constant statistics and noises with unknown time-varying statistics. Simulation and experimental results prove the efficiency of the presented filter in comparison with the previous version.

Last updated on 2020-20-03 at 10:03