Keywords: maglev vehicle, nonlinear dynamics, model predictive control, state constrains, recursive radial basis function, neural network.

The levitation control system plays a pivotal role in governing the intricate aerodynamic load-vehicle-rail coupling vibration process, a crucial determinant of vehicle stability. As speed escalates, the impact of aerodynamic load and vehicle-rail coupling on levitation stability becomes increasingly undeniable. Conventional proportional-integral-derivative (PID) controllers, while effective in simpler environments, exhibit diminished performance within the complexities of high-speed mechanical systems. To address the pressing need for accurate prediction of non-stationary aerodynamic performance in high-speed maglev vehicles, we propose a load prediction model based on the Recursive Radial Basis Function Neural Network (RRBF). This model, leveraging recursive history information in loop neurons, offers dynamic memory capabilities, thus enhancing its learning of temporal patterns. The RRBF network predicts real-time aerodynamic load based on time series states. Simultaneously, we introduce a robust Nonlinear Model Predictive Controller (NMPC), designed to consider the physical constraints of the chopper and the influence of aerodynamic loads on the model's future dynamic behaviours. The algorithm ensures optimal roll optimization within finite time, accounting for constraints. Stability assessments of an electromagnet under non-stationary aerodynamic loads and random track irregularities are conducted through a minimum levitation unit dynamics-control co-simulation model. Results highlight the RRBF's precise identification of aerodynamic loads. Moreover, the Recursive Radial Basis Function Neural Network-based Model Predictive Controller (RMPC) demonstrates superiority over the PID method, showcasing exceptional disturbance resistance and making it more suitable for high-speed maglev levitation control.

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