Gaussian process dynamic modeling and backstepping sliding mode control for magnetic levitation system of maglev train

The maglev trains are strongly nonlinear and open-loop unstable systems with external disturbances and parameters uncertainty. In this paper, the Gaussian process method is utilized to get the dynamic parameters, and a backstepping sliding mode controller is proposed for magnetic levitation systems (MLS) of maglev trains. That is, for a MLS of a maglev train, a nonlinear dynamic model with accurate parameters is obtained by the Gaussian process regression method, based on which a novel robust control algorithm is designed. Specifically, the MLS is divided into two sub-systems by a backstepping method. The inter virtual control inputs and the Lyapunov function are constructed in the first sub-system. For the second sub-system, the sliding mode surface is constructed to fulfil the design of the whole controller to asymptotically regulate the airgap to a desired trajectory. The stability of the proposed control method is analyzed by the Lyapunov method. Both simulation and experimental results are included to illustrate the superior performance of the presented method to cope with parameters perturbations and external disturbance