Modeling and Parameter Identification of the MR Damper Based on LS-SVM
In order to identify the nonlinear characteristics of the magnetorheological (MR) damper applied in multi-DOF vibration reduction platforms in the aerospace field in the modeling process, the least square support vector machine (LS-SVM) method is adopted, because LS-SVM can handle small-sample, high...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | International Journal of Aerospace Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/6648749 |
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| author | Cheng Qian Xiaoliang Yin Qing Ouyang |
| author_facet | Cheng Qian Xiaoliang Yin Qing Ouyang |
| author_sort | Cheng Qian |
| collection | DOAJ |
| description | In order to identify the nonlinear characteristics of the magnetorheological (MR) damper applied in multi-DOF vibration reduction platforms in the aerospace field in the modeling process, the least square support vector machine (LS-SVM) method is adopted, because LS-SVM can handle small-sample, high-dimensional characteristic problems. Firstly, the theory of the modeling method based on LS-SVM was illustrated including the genetic algorithm (GA) optimization method. Secondly, the characteristic curve of the MR damper was tested based on different conditions. Then, the current and historical input displacement, velocity, and current and the historical output are taken as the input of the LS-SVM model and the damping force of the current output is taken as the output of the model for model training. Meanwhile, the genetic algorithm is introduced to optimize the parameters of the LS-SVM model which affect the accuracy of the model, the penalty factor c=16.48, and the kernel parameter σ=3.39 after optimization. Finally, in order to verify the method adopted in the paper, the Simulink model was simulated in certain input conditions; by comparing the simulation and experimental values of this model, it is found that the maximum error is within 10 N and the average error is around 0.89 N, which is similar to the accuracy obtained in other works of literature, and the correctness of this model is verified. |
| format | Article |
| id | doaj-art-2a8e8e3577024cb5be43719179947d00 |
| institution | Kabale University |
| issn | 1687-5966 1687-5974 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Aerospace Engineering |
| spelling | doaj-art-2a8e8e3577024cb5be43719179947d002025-02-03T06:06:28ZengWileyInternational Journal of Aerospace Engineering1687-59661687-59742021-01-01202110.1155/2021/66487496648749Modeling and Parameter Identification of the MR Damper Based on LS-SVMCheng Qian0Xiaoliang Yin1Qing Ouyang2Jiaxing University, Mechanical and Electrical Engineering College, Zhejiang Jiaxing 314001, ChinaJiaxing University, Mechanical and Electrical Engineering College, Zhejiang Jiaxing 314001, ChinaJiaxing University, Mechanical and Electrical Engineering College, Zhejiang Jiaxing 314001, ChinaIn order to identify the nonlinear characteristics of the magnetorheological (MR) damper applied in multi-DOF vibration reduction platforms in the aerospace field in the modeling process, the least square support vector machine (LS-SVM) method is adopted, because LS-SVM can handle small-sample, high-dimensional characteristic problems. Firstly, the theory of the modeling method based on LS-SVM was illustrated including the genetic algorithm (GA) optimization method. Secondly, the characteristic curve of the MR damper was tested based on different conditions. Then, the current and historical input displacement, velocity, and current and the historical output are taken as the input of the LS-SVM model and the damping force of the current output is taken as the output of the model for model training. Meanwhile, the genetic algorithm is introduced to optimize the parameters of the LS-SVM model which affect the accuracy of the model, the penalty factor c=16.48, and the kernel parameter σ=3.39 after optimization. Finally, in order to verify the method adopted in the paper, the Simulink model was simulated in certain input conditions; by comparing the simulation and experimental values of this model, it is found that the maximum error is within 10 N and the average error is around 0.89 N, which is similar to the accuracy obtained in other works of literature, and the correctness of this model is verified.http://dx.doi.org/10.1155/2021/6648749 |
| spellingShingle | Cheng Qian Xiaoliang Yin Qing Ouyang Modeling and Parameter Identification of the MR Damper Based on LS-SVM International Journal of Aerospace Engineering |
| title | Modeling and Parameter Identification of the MR Damper Based on LS-SVM |
| title_full | Modeling and Parameter Identification of the MR Damper Based on LS-SVM |
| title_fullStr | Modeling and Parameter Identification of the MR Damper Based on LS-SVM |
| title_full_unstemmed | Modeling and Parameter Identification of the MR Damper Based on LS-SVM |
| title_short | Modeling and Parameter Identification of the MR Damper Based on LS-SVM |
| title_sort | modeling and parameter identification of the mr damper based on ls svm |
| url | http://dx.doi.org/10.1155/2021/6648749 |
| work_keys_str_mv | AT chengqian modelingandparameteridentificationofthemrdamperbasedonlssvm AT xiaoliangyin modelingandparameteridentificationofthemrdamperbasedonlssvm AT qingouyang modelingandparameteridentificationofthemrdamperbasedonlssvm |