Abstract:
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.
