Optimal Terminal Iterative Learning Control for the Automatic Train Stop System

The train stop control is a typical set-point control task, where only the final state (i.e., the terminal train stop position) is of concern and specified. For such a control problem, an optimal terminal iterative learning control (TILC) approach is presented in this paper, where the stopping position and initial braking speed are chosen as the terminal system output and the control input, respectively. The controller design only depends on the measured input/output (I/O) data without requiring any modeling information of the train operation system, and the learning gain is updated by the system I/O data iteratively to accommodate the system uncertainties. The monotonic convergence of the terminal tracking error is guaranteed by rigorous mathematical analysis. Extensive simulation results are provided to show the applicability and effectiveness of the proposed approach.