Adaptive neural network control for maglev vehicle systems with time-varying mass and external disturbance

Unexpected disturbance and ever-changing passengers are unfavorable factors that always accompany maglev trains. If not considered or handled properly, they would deteriorate the control system performance significantly and even cause instability. This paper proposes a neural network-based adaptive control approach to stabilize the airgap of the nonlinear maglev vehicle. Meanwhile, the time-varying mass and external disturbance can be estimated accurately. Specifically, to ensure the asymptotic stability of the maglev system, a nonlinear basic control law is developed first. To tackle the uncertainty, a radial basis function neural network is fused into the basic controller, which can recover the unknown mass and disturbance more quickly and accurately. Lyapunov stability techniques are utilized to prove the stability of the whole maglev control system without any linear approximation. The sufficient comparative simulation results are provided to demonstrate that the established control scheme can obtain better levitation performance and achieve a precise estimation of time-varying and disturbance simultaneously. Finally, we build a dSPACE-based single electromagnet suspension test bed to examine its efficacy and practical applicability as well.