Dynamical response model for tanker steering using GEBF ased Fuzzy Neural Networks

In this paper, we propose a novel dynamical response model for tanker steering by using the promising Generalized Ellipsoidal Function Based Fuzzy Neural Network (GEBF-FNN) algorithm. Based on a group of well established nonlinear differential equations for tanker maneuvering dynamics, training data samples are generated for the GEBF-FNN method to online identify the K and T parameters of the tanker response model in the form of Nomoto steering model. The GEBF-FNN model starts with zero fuzzy rules and online recruits efficient fuzzy rules via rule node generation criteria and parameter estimation. As a consequence, it results in a dynamical response model for tanker steering with high accuracy and transparent structures consisting of a group of fuzzy rules. In order to demonstrate that the proposed response model is effective, simulation studies are conducted on typical zig-zag maneuvers. Moreover, comprehensive comparisons are carefully presented. Simulation results indicate that the GEBF-FNN based response model achieves promising performance in terms of approximation and prediction.