A Variable Horizon Model Predictive Control for Magnetorheological Semi-Active Suspension with Air Springs
To improve the characteristics of traditional model predictive control (MPC) semi-active suspension that cannot achieve the optimal suspension control effect under different conditions, a variable horizon model predictive control (VHMPC) method is devised for magnetorheological semi-active suspensio...
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MDPI AG
2024-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/21/6926 |
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| author | Gang Li Lin Zhong Wenjun Sun Shaohua Zhang Qianjie Liu Qingsheng Huang Guoliang Hu |
| author_facet | Gang Li Lin Zhong Wenjun Sun Shaohua Zhang Qianjie Liu Qingsheng Huang Guoliang Hu |
| author_sort | Gang Li |
| collection | DOAJ |
| description | To improve the characteristics of traditional model predictive control (MPC) semi-active suspension that cannot achieve the optimal suspension control effect under different conditions, a variable horizon model predictive control (VHMPC) method is devised for magnetorheological semi-active suspension with air springs. Mathematical models are established for the magnetorheological dampers and air springs. Based on the improved hyperbolic tangent model, a forward model is established for the magnetorheological damper. The adaptive fuzzy neural network method is used to establish the inverse model of the magnetorheological damper. The relationship between different road excitation frequencies and the control effect of magnetorheological semi-active suspension with air springs is simulated, and the optimal prediction horizons under different conditions are obtained. The VHMPC method is designed to automatically switch the predictive horizon according to the road surface excitation frequency. The results demonstrate that under mixed conditions, compared with the traditional MPC, the VHMPC can improve the smoothness of the suspension by 2.614% and reduce the positive and negative peaks of the vertical vibration acceleration by 11.849% and 6.938%, respectively. Under variable speed road conditions, VHMPC improved the sprung mass acceleration, dynamic tire deformation, and suspension deflection by 7.191%, 7.936%, and 22.222%, respectively, compared to MPC. |
| format | Article |
| id | doaj-art-d0c1e2ce1b0b46629d17ac2bd1fc35b3 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-d0c1e2ce1b0b46629d17ac2bd1fc35b32025-08-20T02:13:19ZengMDPI AGSensors1424-82202024-10-012421692610.3390/s24216926A Variable Horizon Model Predictive Control for Magnetorheological Semi-Active Suspension with Air SpringsGang Li0Lin Zhong1Wenjun Sun2Shaohua Zhang3Qianjie Liu4Qingsheng Huang5Guoliang Hu6Key Laboratory of Vehicle Intelligent Equipment and Control of Nanchang City, East China Jiaotong University, Nanchang 330013, ChinaKey Laboratory of Vehicle Intelligent Equipment and Control of Nanchang City, East China Jiaotong University, Nanchang 330013, ChinaKey Laboratory of Vehicle Intelligent Equipment and Control of Nanchang City, East China Jiaotong University, Nanchang 330013, ChinaKey Laboratory of Vehicle Intelligent Equipment and Control of Nanchang City, East China Jiaotong University, Nanchang 330013, ChinaKey Laboratory of Vehicle Intelligent Equipment and Control of Nanchang City, East China Jiaotong University, Nanchang 330013, ChinaKey Laboratory of Vehicle Intelligent Equipment and Control of Nanchang City, East China Jiaotong University, Nanchang 330013, ChinaKey Laboratory of Vehicle Intelligent Equipment and Control of Nanchang City, East China Jiaotong University, Nanchang 330013, ChinaTo improve the characteristics of traditional model predictive control (MPC) semi-active suspension that cannot achieve the optimal suspension control effect under different conditions, a variable horizon model predictive control (VHMPC) method is devised for magnetorheological semi-active suspension with air springs. Mathematical models are established for the magnetorheological dampers and air springs. Based on the improved hyperbolic tangent model, a forward model is established for the magnetorheological damper. The adaptive fuzzy neural network method is used to establish the inverse model of the magnetorheological damper. The relationship between different road excitation frequencies and the control effect of magnetorheological semi-active suspension with air springs is simulated, and the optimal prediction horizons under different conditions are obtained. The VHMPC method is designed to automatically switch the predictive horizon according to the road surface excitation frequency. The results demonstrate that under mixed conditions, compared with the traditional MPC, the VHMPC can improve the smoothness of the suspension by 2.614% and reduce the positive and negative peaks of the vertical vibration acceleration by 11.849% and 6.938%, respectively. Under variable speed road conditions, VHMPC improved the sprung mass acceleration, dynamic tire deformation, and suspension deflection by 7.191%, 7.936%, and 22.222%, respectively, compared to MPC.https://www.mdpi.com/1424-8220/24/21/6926magnetorheological dampersemi active suspensionmodel predictive controlvariable horizonair spring |
| spellingShingle | Gang Li Lin Zhong Wenjun Sun Shaohua Zhang Qianjie Liu Qingsheng Huang Guoliang Hu A Variable Horizon Model Predictive Control for Magnetorheological Semi-Active Suspension with Air Springs Sensors magnetorheological damper semi active suspension model predictive control variable horizon air spring |
| title | A Variable Horizon Model Predictive Control for Magnetorheological Semi-Active Suspension with Air Springs |
| title_full | A Variable Horizon Model Predictive Control for Magnetorheological Semi-Active Suspension with Air Springs |
| title_fullStr | A Variable Horizon Model Predictive Control for Magnetorheological Semi-Active Suspension with Air Springs |
| title_full_unstemmed | A Variable Horizon Model Predictive Control for Magnetorheological Semi-Active Suspension with Air Springs |
| title_short | A Variable Horizon Model Predictive Control for Magnetorheological Semi-Active Suspension with Air Springs |
| title_sort | variable horizon model predictive control for magnetorheological semi active suspension with air springs |
| topic | magnetorheological damper semi active suspension model predictive control variable horizon air spring |
| url | https://www.mdpi.com/1424-8220/24/21/6926 |
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