- 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: | SHEN Lu, ZHANG Liwei, ZHANG Menglei. Fuzzy Dual-Adaptive Zero-Power Control for Permanent Electromagnetic Magnet Hybrid Suspension System[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20240632
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A fuzzy dual-adaptive zero-power control method based on a high-order sliding mode observer was proposed to address issues of saturation, response lag, and insufficient disturbance rejection caused by current integration in the zero-power control of permanent magnet electromagnetic hybrid suspension systems. The method comprehensively considered both no-load lifting and load variation operating conditions. First, based on the system mathematical model, a high-order sliding mode observer was designed to estimate the lumped disturbance and error variation rate. Second, feedforward compensation was introduced into the proportional derivative (PD) controller according to the observer output, achieving fast and stable tracking of the suspension gap and dynamic compensation of disturbance forces. Further analysis was conducted on the impact of current integration on dynamic and steady-state performance under both no-load lifting and load variation conditions. Finally, a fuzzy dual-adaptive algorithm was proposed. A two-dimensional fuzzy algorithm was used to optimize the integral coefficient of the current loop online, while the learning rate was dynamically adjusted based on a hyperbolic tangent function, enabling adaptive adjustment of the integral gain weight according to the system dynamics. This effectively suppressed integral saturation and improved system response speed. The research results show that under no-load lifting conditions, the simulation and experimental response time of the proposed method is 0.12 s and 0.25 s, respectively, with no overshoot. Under sudden load variation conditions, the simulation and experimental response time is 0.10 s and 0.15 s, without overshoot. Under continuous load variation conditions, the current error does not exceed ±0.35 A, and no overshoot occurs. Compared with methods using fixed learning rates and fixed current integral coefficients, the proposed method reduces response time by at least 14.2% with zero overshoot.
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