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dc.contributor.authorsharma, sanjay
dc.contributor.authorSutton, R
dc.contributor.authorMotwani, A
dc.contributor.authorAnnamalai, A

<jats:p> Although intrinsically marine craft are known to exhibit non-linear dynamic characteristics, modern marine autopilot system designs continue to be developed based on both linear and non-linear control approaches. This article evaluates two novel non-linear autopilot designs based on non-linear local control network and non-linear model predictive control approaches to establish their effectiveness in terms of control activity expenditure, power consumption and mission duration length under similar operating conditions. From practical point of view, autopilot with less energy consumption would in reality provide the battery-powered vehicle with longer mission duration. The autopilot systems are used to control the non-linear yaw dynamics of an unmanned surface vehicle named Springer. The yaw dynamics of the vehicle being modelled using a multi-layer perceptron-type neural network. Simulation results showed that the autopilot based on local control network method performed better for Springer. Furthermore, on the whole, the local control network methodology can be regarded as a plausible paradigm for marine control system design. </jats:p>

dc.publisherSAGE Publications
dc.subjectUnmanned surface vehicle
dc.subjectautopilot design
dc.subjectnon-linear model predictive control
dc.subjectlocal control network
dc.subjectgenetic algorithm
dc.subjectneural networks
dc.titleNon-linear control algorithms for an unmanned surface vehicle
dc.typeJournal Article
plymouth.journalProceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering/School of Engineering, Computing and Mathematics
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.rights.embargoperiodNot known
rioxxterms.funderEngineering and Physical Sciences Research Council
rioxxterms.identifier.projectAn Intelligent Integrated Navigation and Autopilot System for Uninhabited Surface Vehicles
rioxxterms.typeJournal Article/Review
plymouth.funderAn Intelligent Integrated Navigation and Autopilot System for Uninhabited Surface Vehicles::Engineering and Physical Sciences Research Council

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