An intelligent guidance and control system for ship obstacle avoidance

Abstract

An intelligent guidance and control system using a neurofuzzy network multi-step ahead predictor model is introduced and applied to ship obstacle avoidance, which uses only observed input/output data generated by on-board and external sensors, and a data fusion algorithm to generate the desired way points. A simple and effective way-point guidance scheme based on line of sight is derived for a data-based ship model. A neurofuzzy network predictor, based on using rudder deflection angle for the control of ship heading angle, is utilized on the simulation of an ESSO 190 000 dwt tanker model to demonstrate the effectiveness of the system. The approach is generic and extendable to aircraft and missile control and guidance problems where the vehicle dynamics change significantly during flight in a manner dependent upon operational use, the only requirement for implementation being observed data to construct sensor and vehicle models.