文章基本信息

标题：Nonlinear Innovation-Based Maneuverability Prediction for Marine Vehicles Using an Improved Forgetting Mechanism
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作者：Song, Chunyu ; Zhang, Xianku ; Zhang, Guoqing 等
期刊名称：Journal of Marine Science and Engineering
电子版ISSN：2077-1312
出版年度：2022
卷号：10
期号：9
页码：1-12
DOI：10.3390/jmse10091210
语种：English
出版社：MDPI AG
摘要：This paper carries out marine vehicle maneuverability prediction based on nonlinear innovation. An improved Extended Kalman Filter (EKF) algorithm combined with a forgetting factor is developed by virtue of nonlinear innovation for ship maneuverability using full-scale data. Compared with existing algorithms, the proposed algorithm has high prediction consistency, a good prediction effect, and takes a shorter time to reach the agreement. Furthermore, the real-time prediction data are more than 95% consistent with the actual ship navigation. The forgetting factor is introduced to reduce the cumulative impact of historical interference data. Then, the tangent function is used to process errors; this can solve the problem of inaccurate maneuvering prediction of traditional identification algorithms, making up for the limitations of existing methods. The real-time prediction results are compared with the full-scale data, showing that the proposed ship prediction model has significant prediction accuracy and that the algorithm is reliable. This parameter identification method can be used to establish ship maneuvering prediction models.
关键词：ship maneuvering; nonlinear innovation; real-time prediction; Extended Kalman Filter; forgetting factor; full-scale test