Decoupling Control for Module Suspension System of Maglev Train Based on Feedback Linearization and Extended State Observer

Abstract

The suspension gap of the electromagnetic suspension maglev train is around 8 mm. In practice, it is found that the system gap fluctuations are amplified due to the inner coupling of the suspension module system in the maglev train. In addition, maglev trains are affected by load disturbances and parameter perturbations during operation. These uncertainties reduce the ride comfort. Therefore, it is necessary to propose a novel control strategy to suppress inner coupling while reducing the influence of uncertainties on the system. In this paper, a control strategy based on feedback linearization and extended state observer (ESO) is proposed to address this challenge. Firstly, the suspension module system model is established with parameter uncertainties and external disturbances. Additionally, the inner coupling of the suspension module is represented in this model. Subsequently, the feedback linearization method based on differential geometry theory is applied to reduce the effect of inner coupling. Meanwhile, the system uncertainties are transformed into equivalent disturbances by this method. Afterward, a linear ESO is designed to estimate the equivalent disturbances. Finally, a state feedback controller is used to achieve stable suspension and compensate for the disturbances. Simulation and experimental results show that the proposed decoupled control strategy significantly suppresses the influence of inner coupling and uncertainties on the system compared with the traditional PID control strategy.

A Review of Levitation Control Methods for Low- and Medium-Speed Maglev Systems

“…Furthermore, the method demonstrated satisfactory levitation control even in scenarios characterized by extremely low track stiffness. Li et al [76] designed a controller based on feedback linearization and extended state observer (ESO) techniques to suppress inner coupling effects within the system while mitigating the impact of uncertainties. Han et al [77] proposed a disturbance-observerbased tube model predictive levitation control (DO-TMPLC) scheme based on a linearized feedback model with bounded disturbance.…”