基于强化学习的电磁悬浮型磁浮列车悬浮控制

Reinforcement Learning-based Suspension Control for Electromagnetic Suspension Maglev Trains

摘要:

为了保证磁浮列车的安全、可靠运行，研究了悬浮系统在参数摄动条件下的悬浮控制问题。首先，对电磁悬浮（EMS）型磁浮列车的基本悬浮单元建模，给出了电流控制模型；然后，建立了悬浮系统的强化学习环境以及软演员-评论家（SAC）智能体，并设计了加速训练的奖励函数与“吸死”处理方案；最后，提出了基于强化学习的悬浮控制方法。与传统比例-积分-微分（PID）控制方法的对比结果表明，本方法具有更快的动态响应，在损失50%线圈匝数或磁极面积变化时具有更好的跟踪精度。

Abstract:

In order to ensure the safe and reliable operation of maglev trains， the suspension control of suspension systems under the condition of parameter perturbation was studied. Firstly， the basic suspension unit of a electromagnetic suspension （EMS） maglev train was modeled and the current control model was given. Then， the reinforcement learning environment and the soft actor-critic （SAC）agent were established for the suspension system， and a reward function and an anti-suspension-contact strategy were designed for accelerating the training process. Finally， a corresponding reinforcement learning method was proposed. Compared with the conventional proportional-integral-derivative （PID） control method， the proposed method has faster dynamic response and better tracking accuracy under the condition of 50% loss of coil turns and the pole area change of the magnet.