Iterative Learning Control for High Speed Trains with Nonuniform Operation Lengths

Abstract

In the actual operation of trains, the operation lengths may vary due to schedule changes or emergencies. This paper applies the iterative learning control (ILC) to the problem of a high speed train (HST) to track the reference speed trajectories with nonuniform operation lengths. Two cases of zero initial error and random bounded initial error are considered. An iterative learning law and a parameter updating law are designed to solve the problem. By utilizing the composite energy function (CEF) method, convergence of speed tracking error is rigorously proved. The theoretical results are verified by numerical simulations.