Target Oil Pressure Recognition Algorithm for Oil Pressure Following Control of Electronic Assisted Brake System
The vehicle dynamics model has multiple degrees of freedom, with strong nonlinear characteristics, so it is difficult to quickly obtain the accurate target oil pressure of an electronically assisted brake system based on the model. This paper proposes a target oil pressure recognition algorithm base...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Machines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-1702/11/2/183 |
| _version_ | 1797619732263731200 |
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| author | Lei Chen Yunchen Yu Jie Luo Zhongpeng Xu |
| author_facet | Lei Chen Yunchen Yu Jie Luo Zhongpeng Xu |
| author_sort | Lei Chen |
| collection | DOAJ |
| description | The vehicle dynamics model has multiple degrees of freedom, with strong nonlinear characteristics, so it is difficult to quickly obtain the accurate target oil pressure of an electronically assisted brake system based on the model. This paper proposes a target oil pressure recognition algorithm based on the T-S fuzzy neural network model. Firstly, the braking conditions classification algorithm is built according to the sampled braking intention data. The data are divided into the emergency braking condition data and the general braking condition data by the braking conditions classification algorithm. Secondly, the recognition model is trained respectively by the different braking condition data sets. In the training process, the fuzzy C-means clustering algorithm is used to identify the antecedent parameters of the model, and the learning rate cosine attenuation strategy is applied to optimize the parameter learning process. Finally, a correction method of target oil pressure based on slip ratio is proposed, and the target oil pressure derived following control methods based on traditional PID and fuzzy PID are compared through experiments. The results show that the mean square error of oil pressure following control based on fuzzy PID is smaller, which proves that the proposed method is able to precisely control braking force. |
| first_indexed | 2024-03-11T08:31:03Z |
| format | Article |
| id | doaj.art-0072dfcd9fcf4ba897cda40e14d8c868 |
| institution | Directory Open Access Journal |
| issn | 2075-1702 |
| language | English |
| last_indexed | 2024-03-11T08:31:03Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj.art-0072dfcd9fcf4ba897cda40e14d8c8682023-11-16T21:45:01ZengMDPI AGMachines2075-17022023-01-0111218310.3390/machines11020183Target Oil Pressure Recognition Algorithm for Oil Pressure Following Control of Electronic Assisted Brake SystemLei Chen0Yunchen Yu1Jie Luo2Zhongpeng Xu3School of Mechanical and Electrical Engineering, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Mechanical and Electrical Engineering, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Automation, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Mechanical and Electrical Engineering, Wuhan University of Technology, Wuhan 430070, ChinaThe vehicle dynamics model has multiple degrees of freedom, with strong nonlinear characteristics, so it is difficult to quickly obtain the accurate target oil pressure of an electronically assisted brake system based on the model. This paper proposes a target oil pressure recognition algorithm based on the T-S fuzzy neural network model. Firstly, the braking conditions classification algorithm is built according to the sampled braking intention data. The data are divided into the emergency braking condition data and the general braking condition data by the braking conditions classification algorithm. Secondly, the recognition model is trained respectively by the different braking condition data sets. In the training process, the fuzzy C-means clustering algorithm is used to identify the antecedent parameters of the model, and the learning rate cosine attenuation strategy is applied to optimize the parameter learning process. Finally, a correction method of target oil pressure based on slip ratio is proposed, and the target oil pressure derived following control methods based on traditional PID and fuzzy PID are compared through experiments. The results show that the mean square error of oil pressure following control based on fuzzy PID is smaller, which proves that the proposed method is able to precisely control braking force.https://www.mdpi.com/2075-1702/11/2/183electronically assisted brake systemT-S fuzzy neural networklearning rate cosine attenuation strategyF = fuzzy PID controltarget oil pressure |
| spellingShingle | Lei Chen Yunchen Yu Jie Luo Zhongpeng Xu Target Oil Pressure Recognition Algorithm for Oil Pressure Following Control of Electronic Assisted Brake System Machines electronically assisted brake system T-S fuzzy neural network learning rate cosine attenuation strategy F = fuzzy PID control target oil pressure |
| title | Target Oil Pressure Recognition Algorithm for Oil Pressure Following Control of Electronic Assisted Brake System |
| title_full | Target Oil Pressure Recognition Algorithm for Oil Pressure Following Control of Electronic Assisted Brake System |
| title_fullStr | Target Oil Pressure Recognition Algorithm for Oil Pressure Following Control of Electronic Assisted Brake System |
| title_full_unstemmed | Target Oil Pressure Recognition Algorithm for Oil Pressure Following Control of Electronic Assisted Brake System |
| title_short | Target Oil Pressure Recognition Algorithm for Oil Pressure Following Control of Electronic Assisted Brake System |
| title_sort | target oil pressure recognition algorithm for oil pressure following control of electronic assisted brake system |
| topic | electronically assisted brake system T-S fuzzy neural network learning rate cosine attenuation strategy F = fuzzy PID control target oil pressure |
| url | https://www.mdpi.com/2075-1702/11/2/183 |
| work_keys_str_mv | AT leichen targetoilpressurerecognitionalgorithmforoilpressurefollowingcontrolofelectronicassistedbrakesystem AT yunchenyu targetoilpressurerecognitionalgorithmforoilpressurefollowingcontrolofelectronicassistedbrakesystem AT jieluo targetoilpressurerecognitionalgorithmforoilpressurefollowingcontrolofelectronicassistedbrakesystem AT zhongpengxu targetoilpressurerecognitionalgorithmforoilpressurefollowingcontrolofelectronicassistedbrakesystem |