Abstract:In order to effectively improve the dynamic characteristics of maglev train levitation system under load disturbance and track irregularity disturbance, a radial basis function neural network approximation algorithm based on Lyapunov stability analysis is proposed in this paper, so that the levitation gap can be optimized in a bounded range. Firstly, the vertical dynamic equation and voltage control equation were established by taking the suspended load as the controlled object, and the state space equations were constructed to represent the nonlinearity of the system. Secondly, the basic structure of RBF(Radial basis function) neural network was determined, and the input and output were constructed according to the suspension gap error constraints and control current. The control law was designed to ensure that the output suspension gap can be continuously stable under the combined action of various disturbances; Thirdly, based on the second Lyapunov stability criterion, the closed-loop stability of the system was proved, which can make the gap error converge to infinitesimal in the error tuning process. Finally, the effectiveness of the proposed control algorithm was verified by simulation comparison with PID(proportion- integral-derivative) control algorithm, which is widely used at present. The results show that the proposed control algorithm has better robustness than PID control algorithm