Supervisory predictive control for wheel slip prevention and tracking of desired speed profile in electric trains.

This article presents a supervisory model predictive control system to track the desired speed profile and simultaneously prevent the wheels from slipping in acceleration mode of electrical trains. The proposed control strategy employs field-oriented control (FOC) to control the angular speed of the wheel. Model predictive control (MPC) is used to control the longitudinal velocity of the train to track the desired speed profile and prevent the wheels from slipping by generating the desired angular velocity for the FOC. Since, it is not possible to control the longitudinal velocity and slip ratio independently, a fuzzy supervisor system is proposed to control the train dynamics at the appropriate operating point. A method is presented to estimate train longitudinal velocity and the adhesion coefficient between the wheels and rail surface. These components are vital to implement the proposed method in a real train control system. The closed loop stability of the control system has been studied. Simulations were run under different friction coefficients corresponding to real train parameters to verify the effectiveness of the proposed re-adhesion control system. The simulation results have been compared with the results of other researches to show the feasibility and validity of the presented approach.

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Copyright © 2020. Published by Elsevier Ltd.

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Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.