Model identification and nonlinear adaptive control of suspension system of high-speed maglev train

Abstract

The suspension system of maglev train has intrinsic nonlinear characteristics. The identification processing ability of nonlinear terms and the control accuracy of control algorithm have an important influence on the suspension stability of the train. Especially when the track stiffness is weak, it is easier to affect the suspension stability under the action of small deformation, resulting in the phenomenon of point dropping/rail smashing. Starting from the nonlinear dynamic modelling of maglev suspension system, this paper focuses on the system parameter identification of nonlinear terms and the design of nonlinear control algorithm. Based on Hopfield neural network, the error function and network identification scheme are constructed. In addition, in order to further improve the control accuracy and system robustness, radial basis function (RBF) network adaptive control is carried out based on the identified system model, and the control performance is improved by RBF network approximation principle. Through the numerical simulation analysis, it can be found that the identification effect is good. Moreover, the proposed control algorithm has an obvious effect on improving the control performance and system robustness, which verifies the reliability of the identification results and the control algorithm.