- ISSN 0258-2724
- CN 51-1277/U
- EI Compendex
- Scopus
- Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
- Chinese S&T Journal Citation Reports
- Chinese Science Citation Database
| Citation: | ZHANG Menglei, ZHANG Liwei, SHEN Lu, MENG Xuedong, MA Binrui, LÜ Shangyang. Zero-Power Control of Hybrid Magnetic Levitation System Based on Particle Swarm Optimization[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20260105
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To effectively suppress the airgap overshoot and collision issues during the zero-power control process of the hybrid permanent magnet electromagnetic levitation system, an adaptive super-twisting sliding mode zero-power controller based on current integral feedback was proposed, and the particle swarm optimization (PSO) algorithm was utilized for online tuning of the current integral coefficient. First, the mathematical model of the hybrid levitation system was established, and a zero-power controller was designed based on the current integral feedback strategy. On this basis, a fast non-singular terminal (FNST) sliding mode surface was constructed to accelerate convergence, and an adaptive dual-mode switching strategy was introduced into the super-twisting sliding mode control to form the zero-power controller, realizing rapid airgap adjustment and tracking under disturbances. To address the problems caused by the fixed integral coefficient in the controller, its impact on the dynamic performance of the system was analyzed. The PSO algorithm was utilized for online optimization of the integral coefficient, enabling it to adjust in real time according to the system state, effectively suppressing airgap overshoot, improving convergence speed, and thereby enhancing the overall control performance. Furthermore, to reduce the risk of track collision during the zero-power control process, airgap velocity information was introduced based on the traditional airgap threshold strategy to construct a “velocity + size” dual criterion, enhancing the collision prediction capability in the vertical direction. Simulation and experimental results indicate that the proposed strategy significantly reduces airgap overshoot and accelerates the convergence process. The overshoot is less than 0.30 mm, and the convergence time is shortened to 0.67 s; compared with the traditional threshold method, the proposed dual criterion advances the decision time by approximately 0.10 s, reduces the airgap overshoot by 1.70 mm, and can more effectively predict and prevent the occurrence of collisions.
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