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Abstract

For accurate control and navigation of an autonomous underwater vehicle (AUV) it is criticalto know the water current velocities around the vehicle body. The AUV-onboard acoustic doppler currentprofilers are unable to measure the current near to the vehicle due to their blanking distance, so an AUVmodel-based observer can serve the purpose of estimating the current velocities surrounding the vehicle.In this paper, a high-gain observer based on an AUV dynamics model was used to estimate 3D watercurrent velocities. The water current velocities were determined by calculating the differences betweenthe vehicle velocities over the ground measured by a Doppler velocity log-aided inertial navigation systemand the vehicle velocities through the water estimated by the model-based observer. Modeling and fieldtrials of a Gavia AUV were used to demonstrate the approach. Instead of deriving the roll, pitch, andyaw motions, these were directly given as simulation inputs which allowed the AUV dynamics model tobe simplified to 3-degrees of freedom. This paper presents a real-time model identification algorithm toidentify the nonlinear parameters of the AUV model by utilizing a recursive least squares method. The real timemodel identification algorithm allows the AUV model to be continuously updated in response to theoperational environment. A high-gain observer was chosen as a nonlinear estimation algorithm to obtainthe vehicle velocities through the water, and the Lyapunov stability of the estimation error dynamics wasinvestigated. The observer gain was computed by solving the linear matrix inequality which represented theerror dynamics. By utilizing the observer in the AUV dynamic model, the vehicle's velocity vector throughthe water was estimated, then the current velocity vector was calculated. In order to investigate the differencesbetween the estimated current velocities and the measured current velocities, the standard deviations betweenthese two were quantified. The results showed that the current estimation found by using the model-basedobserver was improved compared with the previous water current estimation method, which found the watervelocity components in a turbulent water column from the AUV motion response.

Item Type:	Article
Authors/Creators:	Kim, E and Fan, S and Bose, N
Keywords:	autonomous underwater vehicle, dynamic modeling, control and estimation, system identification, recursive least squares optimization, model-aided inertial navigation, linear matrix inequality, high-gain observer
Journal or Publication Title:	IEEE Access
Publisher:	Institute of Electrical and Electronics Engineers
ISSN:	2169-3536
DOI / ID Number:	https://doi.org/10.1109/ACCESS.2018.2879469
Copyright Information:	Copyright 2018 IEEE.
Related URLs:	UTAS Profile: Bose, N
Item Statistics:	View statistics for this item

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