http://ir.lib.ruh.ac.lk/handle/iruor/16558 2026-05-03T22:26:19Z 2026-05-03T22:26:19Z http://ir.lib.ruh.ac.lk/handle/iruor/16844 2024-04-17T04:08:50Z 2017-01-05T00:00:00Z

Cover page

2017-01-05T00:00:00Z http://ir.lib.ruh.ac.lk/handle/iruor/16843 2024-04-17T04:05:24Z 2017-01-05T00:00:00Z

Contents

2017-01-05T00:00:00Z http://ir.lib.ruh.ac.lk/handle/iruor/16842 2024-04-17T04:02:36Z 2017-01-05T00:00:00Z

Details

2017-01-05T00:00:00Z Kumara, K.J.C. http://ir.lib.ruh.ac.lk/handle/iruor/16841 2024-04-17T03:58:49Z 2017-01-05T00:00:00Z

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.

2017-01-05T00:00:00Z