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<title>04th ARS - 2017</title>
<link>http://ir.lib.ruh.ac.lk/handle/iruor/16558</link>
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<dc:date>2026-05-03T22:26:03Z</dc:date>
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<title>Cover page</title>
<link>http://ir.lib.ruh.ac.lk/handle/iruor/16844</link>
<description>Cover page
</description>
<dc:date>2017-01-05T00:00:00Z</dc:date>
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<title>Contents</title>
<link>http://ir.lib.ruh.ac.lk/handle/iruor/16843</link>
<description>Contents
</description>
<dc:date>2017-01-05T00:00:00Z</dc:date>
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<item rdf:about="http://ir.lib.ruh.ac.lk/handle/iruor/16842">
<title>Details</title>
<link>http://ir.lib.ruh.ac.lk/handle/iruor/16842</link>
<description>Details
</description>
<dc:date>2017-01-05T00:00:00Z</dc:date>
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<item rdf:about="http://ir.lib.ruh.ac.lk/handle/iruor/16841">
<title>Mathematical Modelling and Dynamic Controlling of an Autonomous Surface Vessel for Fresh Water Applications</title>
<link>http://ir.lib.ruh.ac.lk/handle/iruor/16841</link>
<description>Mathematical Modelling and Dynamic Controlling of an Autonomous Surface Vessel for Fresh Water Applications
Kumara, K.J.C.
An Autonomous Surface Vessel (ASV) can be defined as a vehicle controlling its&#13;
own steeringand speed for navigation, dynamic positioning, motion stabilization,&#13;
obstacle detectionand avoidance. ASV model has been developed here will be&#13;
practically deployed in one of research project of the department of mechanical and&#13;
manufacturing engineering, University of Ruhuna for developing an automated&#13;
weed harvesting boat for fresh water reservoirs in Sri Lanka. The main objective of&#13;
this research is development of a mathematical model for a surface vessel by&#13;
analyzing hydrodynamic forces that will further used to design ordure-learning&#13;
adaptive controller for path tracking. A model-based shape adaptive neural&#13;
network controller is developed by blending a self-adaptive neural network&#13;
module and a classical Proportional plus Derivative (PD)-like control to obtain&#13;
optimum control performance by complementing each other. The adaptive neural&#13;
module counteracts for inherent model discrepancies, strong nonlinearities and&#13;
coupling effects.
</description>
<dc:date>2017-01-05T00:00:00Z</dc:date>
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