高速列车精确停车的鲁棒自触发预测控制

列车精确停车作为列车自动运行(Automatic train operation, ATO)系统的一项核心功能, 对高速列车的安全和高效运行至关重要. 本文针对高速列车停车过程的特点, 考虑在避免控制输出频繁切换的前提下实现高精度的停车曲线跟踪, 提出了基于模型预测控制(Model predictive control, MPC)的精确停车算法. 针对列车停车过程中外部不确定性阻力干扰, 采用鲁棒模型预测控制方法, 提高对外部干扰的鲁棒性. 引入自触发控制策略, 以进一步减少控制输出的频繁切换, 提高停车过程的舒适度. 该方法不需要每个采样时间都求解线性约束二次规划问题, 降低了对系统采样和通信能力的要求, 提高了算法的实用性. 分析结果表明, 高速列车精确停车控制方法的稳定性和性能指标的次优性可以得到保证. 基于高速列车实际运行数据的仿真结果验证了算法的有效性.

Robust Self-triggered Model Predictive Control for Accurate Stopping of High-speed Trains

Accurate stopping is a key technology of the automatic train operation (ATO) system and plays an important role in safe and efficient train operations. This paper presents a model predictive control (MPC) based train stop control method according to the characteristics of train stopping processes, which can realize high-precision speed tracking and avoid frequent switching of control output. Considering the uncertain disturbance from the external environment, a robust MPC method is adopted to improve the robustness for external disturbances. Then, a self-triggered control scheme is introduced to reduce the frequent switches of the control output. Furthermore, the control scheme does not require calculating the linear constraints quadratic programming problem at all times resulting in lower sampling and communication requirements. The stability and sub-optimality of the approach can be guaranteed. The simulation results based on the real-life operation data verify the effectiveness of the proposed method.