一种考虑风阻不确定性的列车速度曲线优化方法
By generating an offline feasible trajectory dataset and offline reinforcement learning, combined with distributed robust optimization theory and wind resistance fuzzy set adversarial training, the high energy consumption and safety issues caused by wind resistance uncertainty in train speed curve optimization are solved, enabling safe, punctual and energy-saving operation of trains in extreme wind disturbance environments.
CN122131621BActive Publication Date: 2026-07-17QINGDAO UNIV OF TECH
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- QINGDAO UNIV OF TECH
- Filing Date
- 2026-05-08
- Publication Date
- 2026-07-17
Smart Images
Figure CN122131621B_ABST
Abstract
本发明涉及轨道交通自动控制技术领域，具体的涉及一种考虑风阻不确定性的列车速度曲线优化方法，包括1基于列车动力学模型和粒子群算法进行冗余时间分配，生成满足运行约束的离线可行轨迹数据集；2采用行为克隆与判别器正则项的离线强化学习框架，利用所述离线可行轨迹数据集训练得到基准控制策略；3基于历史风阻数据构建经验分布，并采用Wasserstein距离度量构建包含真实风阻分布偏差的模糊集；4将所述模糊集作为对抗训练环境，对所述基准控制策略进行最坏风阻分布下的二次强化训练，生成最终的鲁棒最优控制策略。通过本方法，能够使高速列车基本要求的各项性能指标在极端风阻约束范围内达到均衡最优。
Need to check novelty before this filing date? Find Prior Art