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M. Liu^1,2, S. Fu^3,4, H. Wu^1,2, X. Liang^3,4 and X. Zeng^1,2

¹Key Laboratory for Mechanics in Fluid Solid Coupling Systems, Institute of Mechanics, Chinese Academy of Sciences, Beijing, China
²School of Engineering Science, University of Chinese Academy of Sciences, Beijing, China
³Qingdao Sifang Co., Ltd., CRRC, China
⁴State Key Laboratory, High-speed Maglev Transportation Technology, Qingdao, China

Keywords: model predictive control, high-speed maglev train, suspension fluctuation, state prediction, rolling optimization, suspension control.

Abstract

To improve the suspension control effect of the high-speed maglev train, a model predictive control (MPC) algorithm considering the disturbance force is designed based on the electromagnet suspension system unit of the maglev train. When constructing MPC algorithms, the influence of disturbance force on the system is commonly disregarded. However, neglecting this effect may lead to the failure of the predictive model and subsequently impact the performance of the control algorithm. In the paper, the disturbance force is incorporated as input to establish the state prediction model of the suspension system. The predicted values are then corrected by the error between the actual and the predicted state. Subsequently, the objective function and relevant constraints for the suspension system are designed, and the optimal control quantity of the system is obtained by rolling optimization. Finally, the performance of the MPC controller is evaluated through numerical calculations. Research results indicate that, in comparison with the traditional proportional-integral-derivative (PID) feedback controller, the MPC controller proposed effectively suppresses the fluctuation of the suspension gap. Moreover, incorporating the disturbance force into the predictive model of the system's state can further optimize the performance of the MPC controller.

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Front Page Browse CCC CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Conferences ISSN 2753-3239 CCC: 7 PROCEEDINGS OF THE SIXTH INTERNATIONAL CONFERENCE ON RAILWAY TECHNOLOGY: RESEARCH, DEVELOPMENT AND MAINTENANCE Edited by: J. Pombo Paper 16.4 Model Predictive Control of Disturbed Maglev System M. Liu^1,2, S. Fu^3,4, H. Wu^1,2, X. Liang^3,4 and X. Zeng^1,2 ¹Key Laboratory for Mechanics in Fluid Solid Coupling Systems, Institute of Mechanics, Chinese Academy of Sciences, Beijing, China ²School of Engineering Science, University of Chinese Academy of Sciences, Beijing, China ³Qingdao Sifang Co., Ltd., CRRC, China ⁴State Key Laboratory, High-speed Maglev Transportation Technology, Qingdao, China doi:10.4203/ccc.7.16.4 Full Bibliographic Reference for this paper M. Liu, S. Fu, H. Wu, X. Liang, X. Zeng, "Model Predictive Control of Disturbed Maglev System", in J. Pombo, (Editor), "Proceedings of the Sixth International Conference on Railway Technology: Research, Development and Maintenance", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 7, Paper 16.4, 2024, doi:10.4203/ccc.7.16.4 Keywords: model predictive control, high-speed maglev train, suspension fluctuation, state prediction, rolling optimization, suspension control. Abstract To improve the suspension control effect of the high-speed maglev train, a model predictive control (MPC) algorithm considering the disturbance force is designed based on the electromagnet suspension system unit of the maglev train. When constructing MPC algorithms, the influence of disturbance force on the system is commonly disregarded. However, neglecting this effect may lead to the failure of the predictive model and subsequently impact the performance of the control algorithm. In the paper, the disturbance force is incorporated as input to establish the state prediction model of the suspension system. The predicted values are then corrected by the error between the actual and the predicted state. Subsequently, the objective function and relevant constraints for the suspension system are designed, and the optimal control quantity of the system is obtained by rolling optimization. Finally, the performance of the MPC controller is evaluated through numerical calculations. Research results indicate that, in comparison with the traditional proportional-integral-derivative (PID) feedback controller, the MPC controller proposed effectively suppresses the fluctuation of the suspension gap. Moreover, incorporating the disturbance force into the predictive model of the system's state can further optimize the performance of the MPC controller. download the full-text of this paper (PDF, 13 pages, 535 Kb) go to the previous paper go to the next paper return to the table of contents return to the volume description
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