Speed tracking control using an ANFIS model for high-speed electric multiple unit

Abstract The high-speed electric multiple unit (EMU) is a complex, uncertain and nonlinear dynamic system. The traditional approach to operating the high-speed EMU is based upon manual operation. To improve the performance of high-speed EMU, this paper develops a control dynamic model to capture the motion of the high-speed EMU and then uses it to design a desirable speed tracking controller for EMU. We exploit a data-driven adaptive neurofuzzy inference system (ANFIS) to model the running process. Based on the ANFIS model, we propose a generalized predictive control algorithm to ensure the high-precision speed tracking of the high-speed EMU. The simulation results on the actual CRH380AL (China railway high-speed EMU type-380AL) operation data show that the proposed approach could ensure the safe, punctual, comfortable and efficient operation of high-speed EMU.

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