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dc.contributor.authorsharma, sanjay
dc.contributor.authorSutton, R
dc.date.accessioned2017-02-15T16:33:10Z
dc.date.available2017-02-15T16:33:10Z
dc.date.issued2013-04-01
dc.identifier.issn1476-1548
dc.identifier.issn2056-8487
dc.identifier.othern/a
dc.identifier.urihttp://hdl.handle.net/10026.1/8487
dc.description.abstract

There is an increasing drive to develop uninhabited surface vehicles (USV) as cost effective solutions to a number of naval and civilian problems. In part, the resolution of these problems relies upon such vehicles possessing robust guidance and control (GC) systems. Furthermore, the vehicles need to be operated under tight performance specifications satisfying multiple constraints simultaneously. This requires vehicle nonlinearities and constraints to be explicitly considered in the controller design. Nonlinear model predictive control (NMPC) is well suited to satisfy these requirements. This paper reports the design of a novel GC system based on NMPC for use in a USV named Springer which is benchmarked against a linear proportional-integral-derivative counterpart. The NMPC combines a recurrent neural-network model and a genetic-algorithm-based optimiser. Common to the two GC systems is a waypoint line-of-sight (LOS) guidance subsystem. The control objective is to guide the vehicle through different waypoints stored in a mission planner. The performances of the guidance and control systems are evaluated and compared in simulation studies with and without appropriate disturbances. From the results presented, it is concluded that the GC system based on NMPC is more efficient and more capable to guide the vehicle through LOS waypoints particularly in the presence of disturbances.

dc.format.extent29-40
dc.language.isoen
dc.titleA genetic algorithm based nonlinear guidance and control system for an uninhabited surface vehicle
dc.typejournal-article
dc.typeArticle
plymouth.author-urlhttps://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000320015400004&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008
plymouth.issue2
plymouth.volume12
plymouth.publisher-urlhttp://www.ingentaconnect.com/content/imarest/jmet/2013/00000012/00000002/art00004#expand/collapse
plymouth.publication-statusPublished
plymouth.journalJournal of Marine Engineering and Technology
dc.identifier.doi10.1080/20464177.2013.11020276
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA12 Engineering
plymouth.organisational-group/Plymouth/Research Groups
plymouth.organisational-group/Plymouth/Research Groups/Marine Institute
plymouth.organisational-group/Plymouth/Users by role
plymouth.organisational-group/Plymouth/Users by role/Academics
dc.identifier.eissn2056-8487
dc.rights.embargoperiodNot known
rioxxterms.versionofrecord10.1080/20464177.2013.11020276
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.typeJournal Article/Review


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