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| Fractional Order Modeling and Speed Delay Prediction of High-Speed Train High Power Control Process |
| Zhang Kunpeng1,2,3, Yan Fei1,2,3, Yang Hui1,2,3, Liu Hongen4, An Chunlan1,2,3 |
1. School of Electrical and Automation Engineering East China Jiaotong University Nanchang 330013 China;
2. Jiangxi Provincial Key Laboratory of Advanced Control and Optimization Nanchang 330013 China;
3. State Key Laboratory of Performance Monitoring and Protecting of Rail Transit Infrastructure Nanchang 330013 China;
4. Institute of Permanent Magnetic Levitation Technology and Rail Transportation Jiangxi University of Science and Technology Ganzhou 341000 China |
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Abstract The high-power control process of high-speed trains is a complex dynamic system. As the speed of high-speed trains increases, the interactive dynamics among the vehicle, wheel, rail, and grid become frequent. Traditional modeling methods ignore these strong nonlinear differences and only give a single-speed forecasting result at a large spatial scale. It is insufficient to fully describe the micro-changes in the train high-power control process. Recently, some methods have been presented to forecast the time-delay characteristics of high-speed trains. However, most need to consider the common structural characteristics shared by the mechanism and the data-driven models. This paper proposes a fractional modeling strategy to meet the requirements of safe and stable operation of high-speed trains under step-less speed regulation. By learning the optimal control strategy from historical speed and power data, the local train speed for multiple operation scenarios can be forecasted accurately.
Firstly, the time-delay characteristics of high-power traction and braking systems are analyzed based on the features of large inertia and driver reaction time. Secondly, a simplified and redundant process coding strategy for train control strategy is constructed to accurately predict speed time-delay characteristics from the perspective of closed-loop control. Then, a new fractional-order modeling method is proposed, and the fractional-order least squares algorithm is designed to identify the time-varying parameters in the model.
Simulation results on the actual high-speed train operation data show that the V-S (velocity-distance) curve based on the manual control strategy has unstable fluctuations. The control accuracy is 13.1%, which is difficult to meet the multi-objective control range of 8% of the high-speed train automatic driving control strategy, increasing energy consumption and reducing ride comfort. On the other hand, the proposed fractional-order control strategy can effectively reduce the wide range of speed fluctuations. Compared with the benchmark index of high-speed train continuous operation with high power and constant speed, the proposed precision prediction method under the closed-loop structure can meet the safety margin within 3.5% and the control range within 8%. According to the ten-octave equivalent compression property of logarithmic frequency characteristics in automatic control theory, the integer order description and fractional order have equivalent properties for the same high-speed train control system. When the fractional order number is greater than 0.35, the high-speed train speed prediction cannot reach the integer order, and the overfitting effect occurs. Theoretically, realizing the micro speed prediction with a small order is difficult under the current computation power. As a result, the order of fractional least squares is generally set to 0.2.
The following conclusions can be drawn from the simulation analysis. (1) The time-delay characteristics of the high-power control process of high-speed trains are given, and the fine modeling of the operating parameters of the high-speed train control process is realized. (2) The simulation results based on real data show that the proposed method can meet the operational requirements of high-power control of high-speed trains.
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Received: 10 October 2023
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2024, Vol. 39 
