Distributed Learning Control for High-Speed Trains Subject to Operation Safety Constraints
针对高速列车轨迹跟踪中的运行安全约束问题，基于多智能体框架提出分布式学习控制方案，考虑车厢间软硬两种连接模式，通过构建障碍复合能量函数证明跟踪误差收敛与约束满足，并用数值仿真验证。
Trajectory tracking problems related to high-speed trains (HSTs) are fundamental issues that affect the operation safety and ride comfort. This study proposes a distributed learning control scheme based on a multiagent system framework for trajectory tracking of HSTs subject to operation safety constraints. Two different connection modes are considered between the carriages: 1) a soft connection achieved via information exchange and 2) a hard connection enforced via couplers. Both relative displacement and speed constraints are carefully analyzed in the tracking control process. By constructing an appropriate barrier composite energy function, the convergence of the tracking error and satisfaction of the operation safety constraints are rigorously proven for the proposed scheme. The theoretical results are verified using numerical simulations.