[1]包政凯,朱齐丹,刘永超.满秩分解最小二乘法船舶航向模型辨识[J].智能系统学报,2022,17(1):137-143.[doi:10.11992/tis.202104020]
BAO Zhengkai,ZHU Qidan,LIU Yongchao.Ship heading model identification based on full rank decomposition least square method[J].CAAI Transactions on Intelligent Systems,2022,17(1):137-143.[doi:10.11992/tis.202104020]
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BAO Zhengkai,ZHU Qidan,LIU Yongchao.Ship heading model identification based on full rank decomposition least square method[J].CAAI Transactions on Intelligent Systems,2022,17(1):137-143.[doi:10.11992/tis.202104020]
满秩分解最小二乘法船舶航向模型辨识
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
17
期数:
2022年第1期
页码:
137-143
栏目:
学术论文—人工智能基础
出版日期:
2022-01-05
- Title:
- Ship heading model identification based on full rank decomposition least square method
- Keywords:
- forgetting factor least square algorithm; data under excitation; ship heading model; full rank decomposition; parameter identification; ocean environment disturbance; parameter identification convergence; ship navigation data
- 分类号:
- TP18;U661.3
- 摘要:
- 为了解决标准遗忘因子最小二乘法在线辨识船舶航向模型参数漂移和发散问题，考虑到船舶在实际航行中存在海洋环境扰动和数据欠激励的情况，提出并验证了一种基于满秩分解的递推最小二乘法。用实船数据进行船舶航向模型参数辨识，将辨识结果与标准遗忘因子最小二乘算法、多新息最小二乘法、最小二乘支持向量机的辨识结果进行对比，验证了满秩分解有效降低了在线辨识过程中扰动导致的参数漂移并成功抑制了参数的发散，提升了遗忘因子最小二乘法的辨识精度，减小了最小二乘法对持续数据激励的依赖。
- Abstract:
- In order to solve the problem of parameter drift and divergence in the on-line identification of the ship heading model by the forgetting factor least squares method, considering the marine environment disturbance and data under-excitation in actual navigation, we propose a forgetting factor recursive least square algorithm based on full rank decomposition, which uses the ship navigation data to identify the ship heading model parameters, and compare the identification results with the identification results of the standard forgetting factor least squares algorithm, multi-innovation least square algorithm, least square support vector algorithm. The comparison result shows that the full rank decomposition method can effectively reduce the parameter drift caused by the disturbance in the online identification process and successfully suppress divergence of the parameters, improve the accuracy of the forgetting factor least square algorithm and reduce the dependence of the least square method on continuous data excitation.
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备注/Memo
收稿日期:2021-04-11。
基金项目:绿色智能内河船舶创新专项 (MC-202002-C01)；国家自然科学基金项目 (52171299).
作者简介:包政凯，博士研究生，主要研究方向为船舶模型辨识和船舶运动控制;朱齐丹，教授，博士生导师，主要研究方向为智能机器人技术及应用、智能控制系统设计、图像处理与模式识别。现任黑龙江省自动化学会常务理事。主持国家自然科学基金项目、国防973项目、工信部高技术船舶专项项目、科工局国防基础研究重点项目、科技部国际合作项目、海军预研、科研、型号项目等多项。获国家科技进步二等奖1项、国防科技进步一等奖3项、军队科技进步一等奖1项、黑龙江省科技进步二等奖3项，授权发明专利20项、软件著作权5项。发表学术论文200余篇，出版专著4部;刘永超，博士研究生，主要研究方向为非线性自适应控制和船舶运动控制。
通讯作者:朱齐丹. E-mail：zhuqidan@hrbeu.edu.cn
更新日期/Last Update:
1900-01-01