Spatial Adaptive Iterative Learning Tracking Control for High-Speed Trains Considering Passing Through Neutral Sections
针对高速列车通过中性段时存在参数不确定性的速度跟踪问题，提出一种空间迭代学习控制方案，利用空间重复性设计控制律和参数更新律，并通过仿真验证有效性。
This article considers the speed tracking control problem for high-speed train systems (HSTs) under the condition of passing through neutral sections in the presence of parametric uncertainties. Noticing the prominent feature of HSTs operation, i.e., the spatial repetitiveness, a novel spatial iterative learning control (ILC) scheme is proposed. First, the motion dynamic model of HSTs is constructed with the aid of temporal-spatial conversion. Meanwhile, input saturation constraint is introduced to address the limitation of system power supply capability and the loss of traction/braking force in neutral section. Then, the ILC law and the associated parametric updating law are devised to address the system uncertainties and realize the adaptive tracking control simultaneously. The stability of the closed-loop system and the convergence of the tracking errors are confirmed based on a space-weighted Lyapunov–Krasovskii-like composite energy function (CEF). Finally, numerical simulations are performed to illustrate the effectiveness of the proposed control scheme.