An intelligent guidance and control system for ship obstacle avoidance

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Description/Abstract

An intelligent guidance and control system using a neurofuzzy network multistep 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 waypoints. A simple and effective waypoint 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 utilised on the simulation of ESSO 190000dwt 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.