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

Area of Science:

• Marine Engineering
• Control Systems
• System Identification

Background:

• Ship motion modeling faces challenges due to large inertia, coupled systems, and inaccurate data.
• Existing identification methods struggle with unmeasured ship data and scale effects.

Purpose of the Study:

• To propose an improved algorithm for accurate ship system identification.
• To address limitations in modeling complex ship dynamics and data scarcity.

Main Methods:

• Developed an auxiliary model nonlinear innovation least squares identification algorithm.
• Utilized auxiliary model outputs to replace unmeasurable variables in ship test data.
• Optimized error using a tangent function for enhanced accuracy.

Main Results:

• The improved algorithm demonstrated decreased error that continuously approaches zero over time.
• Achieved significantly enhanced identification accuracy and convergence efficiency compared to existing methods.
• Validated the algorithm's reliability for practical application.

Conclusions:

• The proposed identification method offers superior accuracy and efficiency for ship system modeling.
• This technique is applicable to advancing ship intelligent navigation engineering.
• The algorithm effectively overcomes challenges associated with incomplete and inaccurate ship data.