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

Automated summary

This paper proposes a maglev train operation control method based on a fractional-order sliding mode adaptive and diagonal recurrent neural network (FSMA-DRNN) to improve the accuracy of speed profile tracking calculation. By considering three types of resistance during actual train operation, namely air resistance, guide eddy current resistance, and suspension frame generator coil resistance, a kinematic resistance equation is established. The FSMA-DRNN control law and parameter update law are then designed, and a FSMA-DRNN operation controller composed of speed feed forward, fractional-order sliding mode adaptive equivalent control, and diagonal recurrent neural network resistance compensation is developed. The effectiveness and superiority of the proposed approaches are verified through experiments on the high-speed maglev hardware-in-the-loop simulation platform Rt-Lab, using the Shanghai maglev's 29.86 km test line and a five-car train.