Moaveni Bijan, Fathabadi Fatemeh Rashidi, Molavi Ali
Control and System Engineering Group, Faculty of Electrical Engineering, K. N. Toosi University of Technology (KNTU), Tehran, Iran.
School of Railway Engineering, Iran University of Science and Technology (IUST), Tehran, Iran.
ISA Trans. 2020 Jun;101:102-115. doi: 10.1016/j.isatra.2020.01.011. Epub 2020 Jan 16.
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.
本文提出了一种监督模型预测控制系统，用于跟踪期望速度曲线，并在电动列车加速模式下同时防止车轮打滑。所提出的控制策略采用磁场定向控制（FOC）来控制车轮的角速度。模型预测控制（MPC）用于控制列车的纵向速度，通过为FOC生成期望角速度来跟踪期望速度曲线并防止车轮打滑。由于无法独立控制纵向速度和滑移率，因此提出了一种模糊监督系统来在适当的运行点控制列车动力学。提出了一种估计列车纵向速度以及车轮与轨道表面之间粘着系数的方法。这些组件对于在实际列车控制系统中实现所提出的方法至关重要。研究了控制系统的闭环稳定性。在对应于实际列车参数的不同摩擦系数下进行了仿真，以验证所提出的再粘着控制系统的有效性。将仿真结果与其他研究结果进行了比较，以表明所提出方法的可行性和有效性。
