Research on Sliding Mode Control Method of Medium and Low Speed Maglev Train Based on Linear Extended State Observer

Abstract

As a new mode of transportation, maglev train has a broad prospect. As one of the key technologies of maglev train, suspension control technology has been a hot research field. Maglev train has the characteristics of complex system modeling, strong nonlinear model, unstable open-loop system, and complex and changeable working conditions. To date, the widely used method is still the design of PID controller based on linearized maglev model. However, with the passage of time, its shortcomings become increasingly clear. To achieve better control effect, an adaptive sliding mode control method based on linear extended state observer (LESO) is proposed in this paper, which not only improves the response speed of the system, but also improves the robustness and anti-interference ability of the system, and greatly reduces the impact of external disturbances on the suspension stability. First, the dynamic model of the system is established, the control characteristics of the system are analyzed, and the design method of current loop is proposed to reduce the order of the system model. Then, the traditional sliding mode control based on reaching law is designed for the reduced order system, and the performance and shortcomings of the controller are analyzed. Aiming at the defects of traditional sliding mode control, a sliding mode controller based on linear extended state observer is designed. The total disturbance and unmodeled part of the system are estimated in real time by LESO and eliminated in sliding mode control. It can be proved that the designed controller can ensure the stability of the closed-loop system, the system state can converge asymptotically in the neighborhood near the expected value, and has a very fast convergence speed. At the same time, the system has strong robustness to noise and external disturbances. Finally, the effectiveness of the proposed controller is verified by simulation and experiment.