Operation Control Method for High-Speed Maglev Based on Fractional-Order Sliding Mode Adaptive and Diagonal Recurrent Neural Network

The speed profile tracking calculation of high-speed maglev trains is mainly affected by running resistance. In order to reduce the adverse effects and improve accuracy, this paper presents a train operation control method based on fractional-order sliding mode adaptive diagonal recurrent neural network (FSMA-DRNN). First, kinematic resistance equation established due three types that occur during actual train: air resistance, guide eddy current suspension frame generator coil Then, FSMA-DRNN law parameter update are designed, controller composed parts: feed forward, equivalent control, compensation. Furthermore, using designed controller, it proven effective Lyapunov theory for stability closed-loop system. Apart from proposed theoretical analysis, approaches verified experiments hardware-in-the-loop simulation platform Rt-Lab, in line with 29.86 km test five-car Shanghai maglev, showing effectiveness superiority optimization.