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

Wenbai Zhang, Guobin Lin, Keting Hu (), Zhiming Liao and Huan Wang
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Wenbai Zhang: Institute of Rail Transit, Tongji University, Shanghai 201804, China
Guobin Lin: National Maglev Transportation Engineering R&D Center, Tongji University, Shanghai 201804, China
Keting Hu: Institute of Rail Transit, Tongji University, Shanghai 201804, China
Zhiming Liao: National Maglev Transportation Engineering R&D Center, Tongji University, Shanghai 201804, China
Huan Wang: National Maglev Transportation Engineering R&D Center, Tongji University, Shanghai 201804, China

Energies, 2023, vol. 16, issue 12, 1-15

Abstract: 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.

Keywords: high-speed maglev; speed tracking; running resistance; fractional order; diagonal recurrent neural networks (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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