Extended Kalman Filter Based Identification of Dynamic Model for Underwater Robots

Xiao Liang; Jun Dong Zhang; Wei Li; Lin Fang Su

doi:10.4028/www.scientific.net/AMM.29-32.780

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Extended Kalman Filter Based Identification of Dynamic Model for Underwater Robots
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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 29-32Extended Kalman Filter Based Identification of...

Extended Kalman Filter Based Identification of Dynamic Model for Underwater Robots

Abstract:

In order to obtain a precise mathematical model of underwater robots, model identification based on extended kalman filter is proposed in this paper. Parameter estimation is carried out with experiment data of zigzag motion in ocean experiments, and the hydrodynamic derivatives of underwater robots are identified by using extended kalman filter, and the nonlinear dynamic model of an underwater robot is established. The simulation system based on the model is established to verify the validity. The results show the model is credible, which is very useful for the research of maneuverability and adaptive control of underwater robots.

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Periodical:

Applied Mechanics and Materials (Volumes 29-32)

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780-783

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.29-32.780

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Online since:

August 2010

Authors:

Xiao Liang, Jun Dong Zhang, Wei Li, Lin Fang Su

Keywords:

Extended Kalman Flter (EKF), Model Identification, Parameter Estimation, Underwater Robot

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References

[1] Adam J A, Probing beneath the sea, IEEE Spectrum, vol 22, 1985, pp.55-64.

Google Scholar

[2] Xu Yuru, Xiao Kun, Technology development of autonomous ocean vehicle, Robot, vol 33, , 2007, pp.518-521.

Google Scholar

[3] D.R. Blidberg, Autonomous underwater vehicles: a tool for the ocean, Unmanned Systems, vol 9, 1991, pp.10-15.

Google Scholar

[4] Young H. Kim, Frank L. Lewis, Neural network output feedback control of robot manipulators, IEEE Transactions on Robotics and Automation, vol 15, 1999, pp.301-309.

DOI: 10.1109/70.760351

Google Scholar

[5] Pramod Gupta, Naresh K., Modeling Robot Dynamics Using Dynamic Neural Networks, IFAC System Identification, vol 1, 1997, pp.755-760.

Google Scholar

[6] Kevin A'Hearn, Robert Coppolino, A System Identification Method for Simplifying Dynamics Equations of Motion for Remotely Operated Vehicles and Towed Systems, Proceeding of the International Symposium on Underwater Technology, vol 1, 2000, pp.525-530.

DOI: 10.1109/ut.2000.852602

Google Scholar

[7] Mahmoud R. Hadddara, Yie Wang, Parametric Identification of Manoeuvring for ships, International Shipbuilding Progress, vol 46, 1999, pp.5-27.

Google Scholar

[8] Yunxia Liu, Xiaogong Lin et al., Neural network identification for motion system of AUV, Mechanical engineering and automation, vol 2, 2006, pp.81-83.

Google Scholar