Model-free adaptive robust control method for high-speed trains

Aiming at the robustness issue in high-speed trains operation control, this paper proposes a model-free adaptive control (MFAC) scheme to suppress disturbance. Firstly, the dynamic linearization data model of train system under the action of measurement disturbance is given, and the Kalman filter based on this model is derived under the minimum variance estimation criterion. Then, according to Kalman filter, an anti-interference MFAC scheme is designed. This scheme only needs the input and output data of the controlled system to realize the MFAC of the train under strong disturbance. Finally, the simulation experiment of CRH380A high-speed trains is carried out and compared with the traditional MFAC and the MFAC with attenuation factor: the proposed control algorithm can effectively suppress the measurement disturbance, and can obtain smaller tracking error and larger data signal to noise ratio with better applicability.