Citation: | WANG Zhiqiang, GUO Weipeng, SANG Ziliang, LI Bowen, LONG Zhiqiang, LI Xiaolong. Optimized Control Method for Guidance System of High-Speed Maglev Train[J]. Journal of Southwest Jiaotong University, 2025, 60(4): 833-841, 864. doi: 10.3969/j.issn.0258-2724.20230516 |
To further enhance the control performance of the guidance system for high-speed maglev trains, the guidance system was taken as the research subject, and the design and simulation of a guidance controller were carried out based on the mathematical model of a jointed guidance system. The behavior of the maglev train navigating through curves was analyzed under two operating conditions: different velocities while navigating curves and varying magnitudes of lateral disturbance forces. A mathematical model incorporating these disturbances was developed, and a nominal guidance controller was designed using the linear quadratic regulator (LQR) method. The controller parameters were then optimized using a particle swarm optimization (PSO) algorithm. A simulation model of the guidance system was established, and the system’s responses under the two specific operating conditions were analyzed using a simulation platform. A comparison between the algorithms before and after optimization was conducted. The results indicate that, under simulated disturbance forces of 1 kN, 2 kN, and 3 kN, the fluctuation amplitudes of the guidance gap are reduced by 9.46%, 9.70%, and 11.82%, respectively. Furthermore, the recovery velocity of the guidance gap is improved with the optimized algorithm compared to the pre-optimization version. The optimized algorithm also improves the train’s performance when navigating curves and when subjected to crosswind disturbances.
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