Auxiliary model nonlinear innovation least squares algorithm for identification ship 4-DOF via full-scale test data

Abstract For the ship motion with large inertia coupled system identification modeling inaccuracy, the ship scale effect and the existence of partial unmeasured ship data problems. In this paper, an auxiliary model nonlinear innovation least squares identification algorithm is proposed. The new algorithm uses the output of the auxiliary model instead of the unmeasurable variables in the full-scale test data of the ship, and optimizes the error using the tangent function. Compared with the existing algorithm, the error of the improved algorithm decreases with the increase of time and continuously approaches to zero, which greatly improves the identification accuracy and convergence efficiency. The results show that the improved algorithm has significant identification accuracy and reliability. The identification method designed in this paper can be applied to the field of ship intelligent navigation engineering.