Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/7768
Title: On-line state and parameter estimation of an under-actuated underwater vehicle using a modified dual unscented kalman filter
Authors: Karras, George C.
Kyriakopoulos, Kostas J.
Loizou, Savvas 
Keywords: Algorithms
Computer vision
Linear control systems
Submersibles
Issue Date: 2010
Publisher: IEEE
Source: 23rd IEEE/RSJ International Conference on Intelligent Robots and Systems IROS, 2010, Taipei, Taiwan
Abstract: This paper presents a novel modification of the Dual Unscented Kalman Filter (DUKF) for the on-line concurrent state and parameter estimation. The developed algorithm is successfully applied to an under-actuated underwater vehicle. Like in the case of conventional DUKF the proposed algorithm demonstrates quick convergence of the parameter vector. In addition, experimental results indicate an increased performance when the proposed methodology is utilized. The applicability and performance of the proposed algorithm is experimentally verified by combining the proposed DUKF with a non-linear controller on a modified Videoray ROV in a test tank. The on-line estimation of the vehicle states and dynamic parameters is achieved by fusing data from a Laser Vision System (LVS) and an Inertial Measurement Unit (IMU).
URI: http://ktisis.cut.ac.cy/handle/10488/7768
ISSN: 21530858
DOI: 10.1109/IROS.2010.5648831
Rights: ©2010 IEEE
Appears in Collections:Δημοσιεύσεις σε συνέδρια/Conference papers

Show full item record

SCOPUSTM   
Citations 20

5
checked on May 8, 2017

Page view(s)

6
Last Week
1
Last month
checked on May 24, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.