Dear Spiral user, We’re pleased to introduce Helix, https://helix.imperial.ac.uk/ Imperial’s new repository for research datasets. Please use Helix for all future dataset deposits. There is no change to Spiral - continue depositing as usual. For queries, contact the Research Data Management team at rdm-enquiries@imperial.ac.uk. Kind regards, the Spiral Team. The Spiral team
Improving the Energy Efficiency of Autonomous Underwater Vehicles by Learning to Model Disturbances
File(s)
Kormushev_IROS-2013.pdf (807.95 KB)
Accepted version
Author(s)
Kormushev, Petar
Caldwell, Darwin G
Type
Conference Paper
Abstract
Energy efficiency is one of the main challenges for long-term autonomy of AUVs (Autonomous Underwater Vehicles). We propose a novel approach for improving the energy efficiency of AUV controllers based on the ability to learn which external disturbances can safely be ignored. The proposed learning approach uses adaptive oscillators that are able to learn online the frequency, amplitude and phase of zero-mean periodic external disturbances. Such disturbances occur naturally in open water due to waves, currents, and gravity, but also can be caused by the dynamics and hydrodynamics of the AUV itself. We formulate the theoretical basis of the approach, and demonstrate its abilities on a number of input signals. Further experimental evaluation is conducted using a dynamic model of the Girona 500 AUV in simulation on two important underwater scenarios: hovering and trajectory tracking. The proposed approach shows significant energy-saving capabilities while at the same time maintaining high controller gains. The approach is generic and applicable not only for AUV control, but also for other type of control where periodic disturbances exist and could be accounted for by the controller. © 2013 IEEE.
Date Issued
2013-11
Citation
Proc. IEEE/RSJ Intl Conf. on Intelligent Robots and Systems (IROS), 2013
URI
URL
DOI
ISBN
9781467363587
ISSN
2153-0858
Start Page
3885
End Page
3892
Journal / Book Title
Proc. IEEE/RSJ Intl Conf. on Intelligent Robots and Systems (IROS)
Copyright Statement
© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
License URL