Directional and adaptive forgetting factor based recursive least square algorithms for maneuvering dynamic identification and motion prediction of unmanned surface vessel
In recent years, Unmanned Surface Vessel (USV) has been increasingly widely used in commercial and scientific research fields. The prerequisite of realizing intelligent control and auxiliary decision of USV is to establish accurate mathematical model of maneuvering motion and carry out effective par...
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| Vydané v: | Ocean engineering Ročník 342; s. 122948 |
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| Hlavní autori: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier Ltd
30.12.2025
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| ISSN: | 0029-8018 |
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| Abstract | In recent years, Unmanned Surface Vessel (USV) has been increasingly widely used in commercial and scientific research fields. The prerequisite of realizing intelligent control and auxiliary decision of USV is to establish accurate mathematical model of maneuvering motion and carry out effective parameter identification. In this paper, to overcome the limitations of traditional recursive least squares (FFRLS) in dealing with non-continuous excitation and dynamic change environment, two improved recursive least squares algorithms: directional forgetting recursive least squares algorithm (DFFRLS) and adaptive forgetting recursive least squares algorithm (AFFRLS) are proposed.
In this paper, the training datasets are established by validated CFD simulated turning test and Z-shaped maneuverability test. The parameters of the model are identified by DFFRLS and AFFRLS algorithm, and compared with the traditional FFRLS algorithm. The results show that DFFRLS has higher identification accuracy and robustness through directional decomposition when dealing with abnormal data such as random noise, sensor fault and data mutation. However, the AFFRLS, by adapting the forgetting factor adaptively, makes it possible to be more accurate in the process of data updating, especially when the parameters change rapidly. The research results of this paper show that DFFRLS and AFFRLS algorithms have significant advantages in parameter identification of USVs, especially in specific complex and dynamic environments, which can effectively improve the accuracy and performance of the maneuvering model.
•A DFFRLS algorithm is proposed to effectively address parameter divergence problems under discontinuous excitation.•An AFFRLS algorithm is proposed to overcome the limits of traditional forgetting RLS in rapidly changing environments.•Compared with CFD simulation of Z-maneuvers, the accuracy and stability of two identification-based predictions were verified.•DFFRLS shows strong anti-interference in long-term marine environments, while AFFRLS excels in rapidly changing conditions. |
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| AbstractList | In recent years, Unmanned Surface Vessel (USV) has been increasingly widely used in commercial and scientific research fields. The prerequisite of realizing intelligent control and auxiliary decision of USV is to establish accurate mathematical model of maneuvering motion and carry out effective parameter identification. In this paper, to overcome the limitations of traditional recursive least squares (FFRLS) in dealing with non-continuous excitation and dynamic change environment, two improved recursive least squares algorithms: directional forgetting recursive least squares algorithm (DFFRLS) and adaptive forgetting recursive least squares algorithm (AFFRLS) are proposed.
In this paper, the training datasets are established by validated CFD simulated turning test and Z-shaped maneuverability test. The parameters of the model are identified by DFFRLS and AFFRLS algorithm, and compared with the traditional FFRLS algorithm. The results show that DFFRLS has higher identification accuracy and robustness through directional decomposition when dealing with abnormal data such as random noise, sensor fault and data mutation. However, the AFFRLS, by adapting the forgetting factor adaptively, makes it possible to be more accurate in the process of data updating, especially when the parameters change rapidly. The research results of this paper show that DFFRLS and AFFRLS algorithms have significant advantages in parameter identification of USVs, especially in specific complex and dynamic environments, which can effectively improve the accuracy and performance of the maneuvering model.
•A DFFRLS algorithm is proposed to effectively address parameter divergence problems under discontinuous excitation.•An AFFRLS algorithm is proposed to overcome the limits of traditional forgetting RLS in rapidly changing environments.•Compared with CFD simulation of Z-maneuvers, the accuracy and stability of two identification-based predictions were verified.•DFFRLS shows strong anti-interference in long-term marine environments, while AFFRLS excels in rapidly changing conditions. |
| ArticleNumber | 122948 |
| Author | Yu, Shuhang Tan, Mu Xiang, Xianbo Rao, Kunpeng Soares, Carlos Guedes Xiang, Gong |
| Author_xml | – sequence: 1 givenname: Mu orcidid: 0009-0005-7192-7385 surname: Tan fullname: Tan, Mu organization: School of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan, 430074, China – sequence: 2 givenname: Gong orcidid: 0000-0001-6639-272X surname: Xiang fullname: Xiang, Gong email: gongxiang@hust.edu.cn organization: School of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan, 430074, China – sequence: 3 givenname: Xianbo surname: Xiang fullname: Xiang, Xianbo organization: School of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan, 430074, China – sequence: 4 givenname: Shuhang surname: Yu fullname: Yu, Shuhang organization: School of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan, 430074, China – sequence: 5 givenname: Kunpeng orcidid: 0009-0005-5898-5505 surname: Rao fullname: Rao, Kunpeng organization: School of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan, 430074, China – sequence: 6 givenname: Carlos Guedes orcidid: 0000-0002-8570-4263 surname: Soares fullname: Soares, Carlos Guedes organization: Centre for Marine Technology and Ocean Engineering (CENTEC), Instituto Superior Tecnico, Universidade de Lisboa, Lisboa, Portugal |
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| Keywords | USV Directional forgetting factor Computational fluid dynamic Parameter identification Recursive least squares algorithm Adaptive forgetting factor |
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| SubjectTerms | Adaptive forgetting factor Computational fluid dynamic Directional forgetting factor Parameter identification Recursive least squares algorithm USV |
| Title | Directional and adaptive forgetting factor based recursive least square algorithms for maneuvering dynamic identification and motion prediction of unmanned surface vessel |
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