[1]WANG Xinqi,ZHU Qidan.Research on deck motion prediction algorithm based on output error model optimization[J].CAAI Transactions on Intelligent Systems,2023,18(1):75-85.[doi:10.11992/tis.202203012]
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Research on deck motion prediction algorithm based on output error model optimization

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 18 Number of periods: 2023 1 Page number: 75-85 Column: 学术论文—智能系统 Public date: 2023-01-05
Title:
Research on deck motion prediction algorithm based on output error model optimization
Author(s):
WANG Xinqi;  ZHU Qidan
College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China
Keywords:
large ship; very short-term deck motion prediction; time lag; optimal order pair of the model; system parameter identification; output error model; state variables; auxiliary model recursive least squares algorithm
CLC:
TP391.4
DOI:
10.11992/tis.202203012
Abstract:
This paper presents a method for large ship deck motion prediction for many complex sea conditions, with the purpose of improving the applicability to complex sea conditions in different seas, as well as the recognition and prediction accuracy of the deck motion model. The method describes the dynamics of the deck motion by introducing a time lag processing of the measurement data into the output error model. And the order estimation criterion is introduced to determine the optimal order pair of the model. The auxiliary model recursive least squares (AM-RLS) algorithm is applied to identify the system parameters and estimate the state variables in the output error model. The experimental result shows that the proposed prediction method can improve accuracy of the recursive least squares algorithm by 5.13% in the system parameter identification period and can effectively improve the prediction accuracy of the deck motion’s amplitude and phase by 3.17% in the prediction period. The method has good prediction performance under complex sea conditions and is suitable for the large ship deck motion prediction.
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