Adaptive neural network control for visual servoing of underwater vehicles with pose estimation

Adaptive neural network control for visual servoing of underwater vehicles with pose estimation In this paper, the visual servo control of fully actuated underwater vehicles is investigated by employing a position-based approach. Firstly, the global coordinates and Euler angles of the underwater vehicle with respect to a stationary visual target are estimated by an unscented Kalman filter with the visual measurements of point features, whose coordinates in the global frame attached to the stationary target are precisely known. Then, the adaptive neural network controller is designed for underwater vehicles to track the desired trajectory with estimated global pose information. The convergence of tracking errors is ensured by using a single-hidden-layer neural network, in conjunction with a sliding mode controller, to compensate for dynamic uncertainties and external disturbances. Simulation experiments with an underwater vehicle to track a time-varying trajectory and hold its position at a desired point are provided to demonstrate the performances of the proposed vision-based controller. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Marine Science and Technology Springer Journals

Adaptive neural network control for visual servoing of underwater vehicles with pose estimation

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Publisher
Springer Journals
Copyright
Copyright © 2016 by JASNAOE
Subject
Engineering; Automotive Engineering; Engineering Fluid Dynamics; Engineering Design; Offshore Engineering; Mechanical Engineering
ISSN
0948-4280
eISSN
1437-8213
D.O.I.
10.1007/s00773-016-0426-6
Publisher site
See Article on Publisher Site

Abstract

In this paper, the visual servo control of fully actuated underwater vehicles is investigated by employing a position-based approach. Firstly, the global coordinates and Euler angles of the underwater vehicle with respect to a stationary visual target are estimated by an unscented Kalman filter with the visual measurements of point features, whose coordinates in the global frame attached to the stationary target are precisely known. Then, the adaptive neural network controller is designed for underwater vehicles to track the desired trajectory with estimated global pose information. The convergence of tracking errors is ensured by using a single-hidden-layer neural network, in conjunction with a sliding mode controller, to compensate for dynamic uncertainties and external disturbances. Simulation experiments with an underwater vehicle to track a time-varying trajectory and hold its position at a desired point are provided to demonstrate the performances of the proposed vision-based controller.

Journal

Journal of Marine Science and TechnologySpringer Journals

Published: Dec 26, 2016

References

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