- 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: | WEN Tao, XIA Wentao, ZHOU Xu, LONG Zhiqiang. Data-Driven Parameter Tuning for Maglev Train Levitation System[J]. Journal of Southwest Jiaotong University, 2022, 57(3): 506-513. doi: 10.3969/j.issn.0258-2724.20210792
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In order to solve the controller parameter tuning problem caused by the complex nonlinearity of the maglev train levitation system model, a data-driven fast parameter tuning method for the maglev train levitation system is proposed, which is based on only the input and output data of a single levitation tuning of the levitation system. First, the open-loop instability and complex nonlinearity of the levitation system are analyzed by modeling of the maglev train levitation system. Aiming at the problem of determining the reference model in the virtual reference feedback tuning method, estimation of the closed-loop response is then used to realize the data-driven controller parameter tuning. Considering that the interference noise in the data will affect the tuning of controller parameters, a data noise suppression method of maglev system based on signal projection is proposed. Finally, taking a single-rail levitation system for example, the effectiveness of this data-driven parameter tuning method for maglev train levitation system was verified through the single-rail levitation experiment. The results show that the open-loop instability and complex nonlinearity of the suspension system will bring great difficulties to the rapid adjustment of parameters; the noise suppression method based on signal projection can reduce the variance of noise data by 54.1%; and the parameter tuning method based on data driven technique can quickly set the controller parameters of the suspension system. Compared with the PID feedback control system with only coarse tuning under the initial conditions, the step response overshoot of the system after parameter tuning is reduced by 72.0%, the square error integral (ISE) is reduced by 79.8%, and the absolute error integral (IAE) is reduced by 54.5%.
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