Displacement-Constrained Neural Network Control of Maglev Trains Based on a Multi-Mass-Point Model

Abstract

To address the safety displacement-constrained control problem of maglev trains during operation, this study applied the radial-based neural network control displacement-constrained method to maglev trains based on the multi-mass-point model, and strictly limited the output of maglev train displacement and speed values to keep the overshoot within a given range. Firstly, the dynamics and kinematics of the maglev train were modeled from the perspective of multi-mass modeling. Secondly, the basic structure of the radial-based neural network was determined according to the displacement-limited constraints of the maglev train during operation, and the stability was proven by applying the control rate and output-limited priming according to the limitations. Finally, based on the displacement-limited operation control of maglev trains, the system of the radial-based neural network was simulated. The simulation results show that this method can make the displacement and velocity signals of the maglev train converge to the command signals, the target convergence position is reached rapidly, and the deviation can be kept within a stable range so that the displacement and velocity signals of the maglev train can be limited to the desired safety constraints, which can guarantee the stability and safety of the maglev transportation system in the operation process.