Iterative learning control based ramp metering tracking iteration-varying trajectories with internal model

In this work, we focus on iterative learning control (ILC) based ramp metering for tracking iteration-varying trajectories which are described by an internal model between two consecutive iterations. The internal model is inserted into the ILC law, which is different from the classical one. A dynamic linearization of the freeway macroscopic traffic flow model along the iteration axis and lifting technique are used for theoretical analysis, which eliminate the needs for identical initialization condition and lambda-norm that are two fundamental ILC issues in the time domain. Intensive simulations show the superiority of this control strategy compared with the classical ILC.