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Robust Adaptive Control of an Uninhabited Surface Vehicle

A. S. K. Annamalai, R. Sutton, C. Yang, P. Culverhouse, S. Sharma, Chenguang Yang

Journal of Intelligent & Robotic Systems, Volume: 78, Issue: 2, Pages: 319 - 338

Swansea University Author: Chenguang Yang

DOI (Published version): 10.1007/s10846-014-0057-2

Abstract

In this paper, we develop a novel and robust adaptive autopilot for uninhabited surface vehicles (USV). In practice, usually asudden change in dynamics results in aborted missions and the USV has to be rescued to avoid possible damage to other marine crafts inthe vicinity. This problem has been inve...

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Published in: Journal of Intelligent & Robotic Systems
Published: 2015
URI: https://cronfa.swan.ac.uk/Record/cronfa27028
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spelling 2018-01-10T17:56:21.0649910 v2 27028 2016-04-01 Robust Adaptive Control of an Uninhabited Surface Vehicle d2a5024448bfac00a9b3890a8404380b Chenguang Yang Chenguang Yang true false 2016-04-01 EEN In this paper, we develop a novel and robust adaptive autopilot for uninhabited surface vehicles (USV). In practice, usually asudden change in dynamics results in aborted missions and the USV has to be rescued to avoid possible damage to other marine crafts inthe vicinity. This problem has been investigated in our innovative design, which enables the autopilot to cope well with significant changes in the system dynamics and empowers USVs to accomplish their desired missions. The model predictivecontrol technique is employed which adopts an online adaptive nature by utilising three algorithms. Even with random initialisation,significant improvements over the gradient descent and least squares approaches have been achieved by the modified weightedleast squares (WLS) method, which periodically reinitialising the covariance matrix. Extensive simulation studies have been performed to test and verify the advantages of the proposed method. Journal Article Journal of Intelligent & Robotic Systems 78 2 319 338 31 12 2015 2015-12-31 10.1007/s10846-014-0057-2 COLLEGE NANME Engineering COLLEGE CODE EEN Swansea University 2018-01-10T17:56:21.0649910 2016-04-01T18:44:56.5413896 College of Engineering Engineering A. S. K. Annamalai 1 R. Sutton 2 C. Yang 3 P. Culverhouse 4 S. Sharma 5 Chenguang Yang 6 0027028-01042016185539.pdf accepted.pdf 2016-04-01T18:55:39.1500000 Output 2710642 application/pdf Accepted Manuscript true 2016-05-17T00:00:00.0000000 true
title Robust Adaptive Control of an Uninhabited Surface Vehicle
spellingShingle Robust Adaptive Control of an Uninhabited Surface Vehicle
Chenguang Yang
title_short Robust Adaptive Control of an Uninhabited Surface Vehicle
title_full Robust Adaptive Control of an Uninhabited Surface Vehicle
title_fullStr Robust Adaptive Control of an Uninhabited Surface Vehicle
title_full_unstemmed Robust Adaptive Control of an Uninhabited Surface Vehicle
title_sort Robust Adaptive Control of an Uninhabited Surface Vehicle
author_id_str_mv d2a5024448bfac00a9b3890a8404380b
author_id_fullname_str_mv d2a5024448bfac00a9b3890a8404380b_***_Chenguang Yang
author Chenguang Yang
author2 A. S. K. Annamalai
R. Sutton
C. Yang
P. Culverhouse
S. Sharma
Chenguang Yang
format Journal article
container_title Journal of Intelligent & Robotic Systems
container_volume 78
container_issue 2
container_start_page 319
publishDate 2015
institution Swansea University
doi_str_mv 10.1007/s10846-014-0057-2
college_str College of Engineering
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hierarchy_top_id collegeofengineering
hierarchy_top_title College of Engineering
hierarchy_parent_id collegeofengineering
hierarchy_parent_title College of Engineering
department_str Engineering{{{_:::_}}}College of Engineering{{{_:::_}}}Engineering
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description In this paper, we develop a novel and robust adaptive autopilot for uninhabited surface vehicles (USV). In practice, usually asudden change in dynamics results in aborted missions and the USV has to be rescued to avoid possible damage to other marine crafts inthe vicinity. This problem has been investigated in our innovative design, which enables the autopilot to cope well with significant changes in the system dynamics and empowers USVs to accomplish their desired missions. The model predictivecontrol technique is employed which adopts an online adaptive nature by utilising three algorithms. Even with random initialisation,significant improvements over the gradient descent and least squares approaches have been achieved by the modified weightedleast squares (WLS) method, which periodically reinitialising the covariance matrix. Extensive simulation studies have been performed to test and verify the advantages of the proposed method.
published_date 2015-12-31T03:38:13Z
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score 10.887916