Control Optimization of a Hybrid Magnetic Suspension Blood Pump Controller Based on the Finite Element Method
This study focuses on a blood pump system equipped with four radial active magnetic bearings (RAMBs). The finite element method (FEM) was employed to optimize the physical parameters of the system. Based on this optimization, two intelligent PID tuning strategies—particle swarm optimization (PSO) an...
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MDPI AG
2025-06-01
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| Online Access: | https://www.mdpi.com/2075-1702/13/7/567 |
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| author | Teng Jing Yu Yang Weimin Ru |
| author_facet | Teng Jing Yu Yang Weimin Ru |
| author_sort | Teng Jing |
| collection | DOAJ |
| description | This study focuses on a blood pump system equipped with four radial active magnetic bearings (RAMBs). The finite element method (FEM) was employed to optimize the physical parameters of the system. Based on this optimization, two intelligent PID tuning strategies—particle swarm optimization (PSO) and backpropagation (BP) neural networks—were compared. First, a differential control model of a single-degree-of-freedom active magnetic bearing was developed, based on the topology and operating principles of the radial magnetic bearings. Then, magnetic circuit parameters were precisely identified through finite element simulation, enabling accurate optimization of the physical model. To enhance control accuracy, intelligent tuning strategies based on PSO and BP neural networks were applied, effectively addressing the limitations of conventional PID controllers, which often rely on empirical tuning and lack precision. Finally, simulation experiments were conducted to evaluate the optimization performance of PSO and BP neural networks in the magnetic bearing control system. The results demonstrate that the improved PSO algorithm offers significant advantages over both the BP neural network and traditional manual PID tuning. Specifically, it achieved a rise time of 0.0049 s, a settling time of 0.0079 s, and a steady-state error of 0.0013 mm. The improved PSO algorithm ensures system stability while delivering faster dynamic response and superior control accuracy. |
| format | Article |
| id | doaj-art-98fcaddc4e634ade992dbb8b49e61c5a |
| institution | DOAJ |
| issn | 2075-1702 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj-art-98fcaddc4e634ade992dbb8b49e61c5a2025-08-20T02:45:42ZengMDPI AGMachines2075-17022025-06-0113756710.3390/machines13070567Control Optimization of a Hybrid Magnetic Suspension Blood Pump Controller Based on the Finite Element MethodTeng Jing0Yu Yang1Weimin Ru2Research Center of Fluid Machinery Engineering and Technology, Jiangsu University, Zhenjiang 212013, ChinaResearch Center of Fluid Machinery Engineering and Technology, Jiangsu University, Zhenjiang 212013, ChinaResearch Center of Fluid Machinery Engineering and Technology, Jiangsu University, Zhenjiang 212013, ChinaThis study focuses on a blood pump system equipped with four radial active magnetic bearings (RAMBs). The finite element method (FEM) was employed to optimize the physical parameters of the system. Based on this optimization, two intelligent PID tuning strategies—particle swarm optimization (PSO) and backpropagation (BP) neural networks—were compared. First, a differential control model of a single-degree-of-freedom active magnetic bearing was developed, based on the topology and operating principles of the radial magnetic bearings. Then, magnetic circuit parameters were precisely identified through finite element simulation, enabling accurate optimization of the physical model. To enhance control accuracy, intelligent tuning strategies based on PSO and BP neural networks were applied, effectively addressing the limitations of conventional PID controllers, which often rely on empirical tuning and lack precision. Finally, simulation experiments were conducted to evaluate the optimization performance of PSO and BP neural networks in the magnetic bearing control system. The results demonstrate that the improved PSO algorithm offers significant advantages over both the BP neural network and traditional manual PID tuning. Specifically, it achieved a rise time of 0.0049 s, a settling time of 0.0079 s, and a steady-state error of 0.0013 mm. The improved PSO algorithm ensures system stability while delivering faster dynamic response and superior control accuracy.https://www.mdpi.com/2075-1702/13/7/567radial active magnetic bearingsfinite element methodbackpropagation |
| spellingShingle | Teng Jing Yu Yang Weimin Ru Control Optimization of a Hybrid Magnetic Suspension Blood Pump Controller Based on the Finite Element Method Machines radial active magnetic bearings finite element method backpropagation |
| title | Control Optimization of a Hybrid Magnetic Suspension Blood Pump Controller Based on the Finite Element Method |
| title_full | Control Optimization of a Hybrid Magnetic Suspension Blood Pump Controller Based on the Finite Element Method |
| title_fullStr | Control Optimization of a Hybrid Magnetic Suspension Blood Pump Controller Based on the Finite Element Method |
| title_full_unstemmed | Control Optimization of a Hybrid Magnetic Suspension Blood Pump Controller Based on the Finite Element Method |
| title_short | Control Optimization of a Hybrid Magnetic Suspension Blood Pump Controller Based on the Finite Element Method |
| title_sort | control optimization of a hybrid magnetic suspension blood pump controller based on the finite element method |
| topic | radial active magnetic bearings finite element method backpropagation |
| url | https://www.mdpi.com/2075-1702/13/7/567 |
| work_keys_str_mv | AT tengjing controloptimizationofahybridmagneticsuspensionbloodpumpcontrollerbasedonthefiniteelementmethod AT yuyang controloptimizationofahybridmagneticsuspensionbloodpumpcontrollerbasedonthefiniteelementmethod AT weiminru controloptimizationofahybridmagneticsuspensionbloodpumpcontrollerbasedonthefiniteelementmethod |