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

Chunyu Song1, Yinfu Li1, Jianghua Sui2

  • 1Navigation and Ship Engineering College, Dalian Ocean University, Dalian, 116023, China.

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This study introduces a new algorithm for ship system identification, improving accuracy and efficiency in modeling complex ship motion dynamics. The method enhances data utilization for more reliable ship intelligent navigation engineering.

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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.