Anti-Slip Re-Adhesion Control Strategy of Electric Locomotive Based on Distributed MPC

Xu, Wen; Huang, Jingchun; Zhang, Shuo

doi:10.1109/hpcc/smartcity/dss.2019.00380

Cited by 11 publications

(7 citation statements)

References 6 publications

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“…Their control strategy was simulated and implemented in a test rig. Wen et al [34] proposed distributed Model Predictive Control (MPC) as a re-adhesion control system in four axes of the electric locomotive.…”

Section: Control Strategies Based On the Slip Estimationmentioning

confidence: 99%

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Fuzzy control system design for wheel slip prevention and tracking of desired speed profile in electric trains

Moaveni

Fathabadi

Molavi

2020

Asian Journal of Control

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This study proposes an anti-slip control system for electric trains based on the fuzzy logic theory, which prevents the wheels from slipping during the acceleration and simultaneously tracks the desired speed profile. To improve the control performance, the train longitudinal velocity and the slip ratio are estimated. By using a Field Oriented Control (FOC), the angular speed of the traction motor is controlled. The fuzzy control system determines the desired angular speed of the traction motor as the reference input of FOC to obtain the desired slip ratio and track the desired speed profile. Simulation results show the effectiveness of the control system in various wheel-rail surface conditions based on the real parameters of ER24PC locomotive.

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Section: Control Strategies Based On the Slip Estimationmentioning

confidence: 99%

“…)-(34), it is assumed that parameters m have known accurate values. Also, it is assumed that stator currents and voltages, and angular speed of the wheel are accurately measurable.…”

mentioning

confidence: 99%

Fuzzy control system design for wheel slip prevention and tracking of desired speed profile in electric trains

Moaveni

Fathabadi

Molavi

2020

Asian Journal of Control

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“…Model predictive control, as an optimal control algorithm based on models, is effective in handling complex linear or nonlinear systems with constraints. Linear time-varying model predictive control is implemented in real-time by linearizing the stable region of the wheel-rail adhesion characteristic curve and controlling it using a linear time-varying model predictive control system [8]. This method used can only linearize the modeling in the stable region.…”

Section: Introductionmentioning

confidence: 99%

Research on optimal train adhesion control based on nonlinear model predictive control

He,

Wang,

Balogun Alanamu

2024

International Conference on Smart Transportation and City Engineering (STCE 2023)

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The phenomenon of wheel idling, resulting from poor adhesion conditions, not only reduces traction utilization but also poses a safety risk to train operation. A unique peak adhesion point is observed in the wheel-rail adhesion characteristic curve. To maintain stable train operation at this peak adhesion point, it is crucial to enhance adhesion utilization. This paper proposes a nonlinear model predictive control algorithm for optimizing train adhesion control. Initially, the dynamic forgetting factor recursive least squares algorithm is employed to online identify track parameters. These identified online track parameters are subsequently utilized by the nonlinear model predictive controller to achieve optimal adhesion control. The aim is to position the train's operation point as closely as possible to the adhesion curve's peak, thereby achieving optimal adhesion control. A MATLAB/Simulink simulation model for vehicle wheel-rail adhesion is developed, and the simulation results demonstrate the superior effectiveness of the proposed adhesion control method when compared to linear time-varying model prediction adhesion control. Additionally, a higher average adhesion utilization rate is achieved.

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“…However, the working point range of observation frequency is very small, which limits the application of phase shift method in practice. Now more and more researches are beginning to focus on the application of intelligent control algorithms in anti-slip control, such as sliding mode control [9], fuzzy logic [10], predictive control [11], etc. Among them, sliding mode control is widely used, has strong robustness performance, and is suitable for nonlinear and uncertain systems.…”

Section: Introductionmentioning

confidence: 99%

Anti-slip optimization control strategy for high-speed train

Yang,

Huang,

Dai

et al. 2024

International Conference on Smart Transportation and City Engineering (STCE 2023)

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In response to the problem of low adhesion utilization in traditional anti-slip control, this paper designs an anti-slip optimization control strategy for high-speed train. The full-order closed-loop state observer is used to estimate the rail surface adhesion coefficient in real time, and the real-time search of adhesion peak point is realized by combining slip velocity and other information. The sliding mode control algorithm dynamically adjusts the motor torque to track the maximum adhesion point, which improves the robustness of the system. A sliding mode control algorithm based on Power Rate Exponential Reaching Law (PRERL) is proposed to address the chattering problem of traditional sliding mode controllers. Simulation verification was conducted on the MATLAB/Simulink model, and the results showed that the proposed anti-slip control strategy had good control effect and achieved the high adhesion utilization.

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Anti-Slip Re-Adhesion Control Strategy of Electric Locomotive Based on Distributed MPC

Cited by 11 publications

References 6 publications

Fuzzy control system design for wheel slip prevention and tracking of desired speed profile in electric trains

Fuzzy control system design for wheel slip prevention and tracking of desired speed profile in electric trains

Research on optimal train adhesion control based on nonlinear model predictive control

Anti-slip optimization control strategy for high-speed train

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