LAUSR

Maglev train is a new vehicle of rail transit. The time-varying load caused by large capacity is an important test of suspension system. If the state variable matrix and parameter matrix caused by time-varying mass cannot be estimated effectively, it is very likely to lead to the decline of suspension performance and instability. Firstly, an NNs state observer based on neural network is proposed to effectively estimate the system state and parameter matrix. Secondly, based on the NNs observer model, an inverse control algorithm with stronger regulation ability is designed by integrating the output limited characteristics and Lyapunov function, which can ensure the stability of the system when disturbed. The simulation results show that the control algorithm proposed in this paper can have faster response speed and stability in the flotation process of 16mm-10mm, and has better anti-disturbance ability in the face of time-varying mass disturbance. Finally, based on the single point suspension experimental platform, the effectiveness and practicability of the proposed control algorithm are verified, and the correctness of the proposed NNs observer model is also verified.