Iterative learning control of freeway flow via ramp metering and simulation on PARAMICS

This paper proposes an iterative learning control approach for the traffic control problem and the objective of control is to derive a proper control signal through iterative learning process to achieve the freeway mainline density tracking task in the presence of unknown but repeated environment and disturbance. The performance of the proposed approach is evaluated based on simulation results on the microscopic traffic simulation software PARAMICS and comparison with the local feedback controller ALINEA. The paper concludes that iterative learning control based ramp metering can drive the density of traffic flow to track a reference output in the presence of unknown but repeated disturbance.