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The lateral vibration of the car body affects the lateral stability of high-speed trains (HSTs), and the aim of this study is to design a good control strategy to decrease the lateral vibration of the car body. First, the Maxwell model of the anti-yaw damper was transformed into a Voigt model by the equivalence method, and a modified 17-degree-of-freedom (DOF) lateral vibration model that includes the anti-yaw damper was established. After the design difficulty and control accuracy of the control strategy were weighed, the lateral and yaw motions of the wheelsets were regarded as disturbances, and a disturbance observer of wheelset motions was designed to simplify the 17-DOF model to a 9-DOF lateral vibration model. Second, a model predictive control (MPC) strategy for HST lateral vibration was designed, and the effects of the key parameters on the lateral vibration acceleration of the HST car body and on the iterative computation time of the MPC strategy were studied. In addition, a conditionally triggered model predictive control (CMPC) strategy was proposed. Last, the control effects of the CMPC strategy under the working conditions of speed change, turnout passage, and aerodynamic load were studied by simulation, which verified the validity of the proposed model and control strategy. [ABSTRACT FROM AUTHOR]

The setting curve and control method of internal temperature in trains are studied in this paper to achieve energy saving control for air conditioning systems. First, a model of interior temperature that considers the heat transmission from infiltration air is established according to the relationship between the equivalent leakage area for the train body and the pressure difference between the internal and external carriage. Second, the interior temperature setting curve, which changes adaptively with the ambient temperature, is established considering the energy consumption of air conditioning systems and passengers' thermal comfort. Finally, an adaptive iterative learning control (AILC) algorithm is proposed and applied to control the interior temperature according to the periodicity of train operation. In this paper, meteorological data from a typical meteorological year are used for simulation analysis. Simulation results show that the AILC algorithm can effectively track the interior temperature setting value and has a better control effect when the train runs on the same line repeatedly. Under the action of the AILC algorithm, the root-mean-square error is reduced to 0.069 °C in the third iteration. Furthermore, the energy saving for a single carriage in a single process reaches up to 1.534 kW·h, thus achieving energy saving control. [ABSTRACT FROM AUTHOR]