Study of Ship Heading Control using RBF Neural Network

Abstract

Along with the development of shipping business, ships are becoming bigger, faster and more intelligent. Thus better performance of maneuver is demanded. To research for better control strategies, it is necessary to adopt new control theories and techniques. The application of neural network techniques and backstepping algorithm in ship motion control became an important research area in recent years. Aiming at the nonlinear of ship motion, also for application of control strategy, control strategy based on the RBF neural network and backstepping algorithm is proposed. The strategy employs the RBF neural network to approximate and substitute the system, and employs adaptive law designed by backstepping algorithm to adjust the weight of the RBF neural network. Finally, the proposed strategy was applied in ship course tracking control simulation and the satisfying performances demonstrate the feasibility and effectiveness of the ship control strategy.