Nonlinear dynamics analysis of the high-speed maglev vehicle/guideway coupled system with backstepping fuzzy-neural-network control - Hao Zeng; Jingyu Huang; Ziyang Zhang; Qifei Lu (Advances in Mechanical Engineering)
To improve the stability of the maglev train’s levitation and enhance its dynamic performance during high-speed operation, this paper establishes a control strategy based on backstepping control theory and fuzzy neural network. Firstly, this paper presents a vehicle/guideway numerical model based on the Shanghai Maglev line (SML) structure, considering the guideway’s flexible deformation and vertical irregularity. The coupled vibration of the vehicle and guideway is solved using an implicit time-domain analysis method. Then, a backstepping control (BSC) scheme is designed using multi-body dynamics and electromagnetic force equations. To improve its control performance, a fuzzy neural network control strategy (BFNNC) is designed to mimic the BSC scheme to achieve the suspension control of high-speed maglev trains. Finally, the paper confirms the reliability of the numerical model with the measured data of the SML. The dynamic responses of the vehicle and guideway are studied systematically under two different control schemes at high speeds. The control performance of the BFNNC strategy and the BSC scheme is compared considering different train speeds and disturbance force. The conclusion shows that compared to the BSC scheme, the BFNNC strategy has a superior control performance and robustness performance.