Swarm intelligence applied to identification of nonlinear ship steering model

Abstract

The paper presents optimization of parameters of nonlinear dynamic ship steering model with one degree of freedom, in which the input is the commanded rudder angle and the output is the ship course. Optimization of parameters concerned the Bech and Wagner-Smith model for which the nonlinearity was determined from a standard Bech's reverse spiral test, whilst the parameters describing dynamic properties of the ship were determined based on the Kempf's zigzag maneuver. Optimal parameters of the searched ship model were found using swarm intelligence algorithms, including: ant colony optimization, artificial bee colony, and particle swarm optimization. Rate tests were conducted to find the optimal solution, and a comparative analysis of the results was made.