In order to cover the complexity of coding and extend the generality on the road vehicle-bridge iteration, a process to solve vehiclebridge interaction considering varied vehicle speed based on a convenient combination of Matlab Simulink and ANSYS is presented. In this way, the road vehicle is modeled in state space and the corresponding motion equations are solved using Simulink. The finite element model for the bridge is established and solved using ANSYS. The so-called inter-history iteration method is adopted to realize the interaction between the vehicle model and the bridge model. Different from typical method of road vehicle-bridge interaction in the vertical direction, a detailed longitudinal force model is set up to take into account the effects of varied vehicle speed. In the force model, acceleration and braking of the road vehicle are treated differently according to their mechanical nature. In the case studies based on a simply supported beam, the dynamic performance of the road vehicle and the bridge under varied vehicle speeds is calculated and discussed. The vertical acceleration characteristics of the midpoint of beam under varied vehicle speed can be grouped into two periods. The first one is affected by the load transform between the wheels, and the other one depends on the speed amplitude. Sudden change of the vertical acceleration of the beam and the longitudinal reaction force are observed as the wheels move on or off the bridge, and the bridge performs different dynamic responses during acceleration and braking.

Several factors could affect the function of the electromagnet control system when a high-speed maglev train runs over a bridge. To enhance the robustness of the electromagnet control system to the high-speed maglev train running over the bridge, a fuzzy active control rule is introduced into the currently used proportional–integral–derivative (PID) control system. Numerical analyses are then conducted with a high-speed maglev train passing through a series of simply supported beams. The numerical results with the fuzzy PID active control are compared with the maglev train–bridge system with the equivalent linearized electromagnetic forces. The comparative results show that the introduction of the fuzzy PID control system has improved the comfort of the maglev train and that the overall dynamic response of the bridge is reduced. There is an obvious time delay for the maximum dynamic response of the bridge to the high speed of the train.