Abstract
The magnetic levitation system is a critical part to guarantee safe and reliable operations of a maglev train. In this paper, the control strategy is proposed for the magnetic levitation system based on the model predictive control incorporating two-level state feedback. Taking advantage of the measurable state variables, that is, air gap, electromagnet acceleration, and control current through high-resolution sensor measurement, the first-level nonlinear state feedback is to linearize the unstable nonlinear magnetic levitation system, and the second-level linear state feedback is to further stabilize the system and improve the dynamic performances, which together provide a stable prediction model. The simulation results demonstrate that the proposed control strategy can ensure high-precision air gap control and favorable disturbance resistance ability.
Highlights
The electromagnetic suspension system for a magnetic levitation train utilizes the electromagnetic attractive forces between the train body-equipped levitation electromagnets and their above electromagnetic components to make the train body levitated above the guide rails for train longitudinal motions
Zhang et al.12 performed the nonlinear analysis of a maglev control system with time-delayed feedback
Through the two-level state feedback, a stable prediction model is provided to model predictive control (MPC) which globally ameliorates the transient performances especially under disturbances
Summary
The electromagnetic suspension system for a magnetic levitation (maglev) train utilizes the electromagnetic attractive forces between the train body-equipped levitation electromagnets and their above electromagnetic components to make the train body levitated above the guide rails for train longitudinal motions. Keywords Magnetic levitation system, model predictive control, state feedback, nonlinear dynamics, disturbance resistance Through the two-level state feedback, a stable prediction model is provided to MPC which globally ameliorates the transient performances especially under disturbances.
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