http://ir.lib.ruh.ac.lk/handle/iruor/16558 Sun, 03 May 2026 22:25:38 GMT 2026-05-03T22:25:38Z http://ir.lib.ruh.ac.lk/handle/iruor/16844 Cover page Thu, 05 Jan 2017 00:00:00 GMT http://ir.lib.ruh.ac.lk/handle/iruor/16844 2017-01-05T00:00:00Z http://ir.lib.ruh.ac.lk/handle/iruor/16843 Contents Thu, 05 Jan 2017 00:00:00 GMT http://ir.lib.ruh.ac.lk/handle/iruor/16843 2017-01-05T00:00:00Z http://ir.lib.ruh.ac.lk/handle/iruor/16842 Details Thu, 05 Jan 2017 00:00:00 GMT http://ir.lib.ruh.ac.lk/handle/iruor/16842 2017-01-05T00:00:00Z http://ir.lib.ruh.ac.lk/handle/iruor/16841 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 own steeringand speed for navigation, dynamic positioning, motion stabilization, obstacle detectionand avoidance. ASV model has been developed here will be practically deployed in one of research project of the department of mechanical and manufacturing engineering, University of Ruhuna for developing an automated weed harvesting boat for fresh water reservoirs in Sri Lanka. The main objective of this research is development of a mathematical model for a surface vessel by analyzing hydrodynamic forces that will further used to design ordure-learning adaptive controller for path tracking. A model-based shape adaptive neural network controller is developed by blending a self-adaptive neural network module and a classical Proportional plus Derivative (PD)-like control to obtain optimum control performance by complementing each other. The adaptive neural module counteracts for inherent model discrepancies, strong nonlinearities and coupling effects. Thu, 05 Jan 2017 00:00:00 GMT http://ir.lib.ruh.ac.lk/handle/iruor/16841 2017-01-05T00:00:00Z