Freeway Ramp Control Based on Iterative Learning

In this work, we apply the iterative learning method to address the traffic density control problem in a macroscopic level freeway environment with ramp metering. The macroscopic model to describe the evolution of freeway traffic flow is firstly established. Then traffic density is selected as the control variable in place of traffic occupancy, and the control objective is determined. In conjunction with nonlinear feedback theory, the iterative learning based ramp control system is designed. Finally, the system simulation is carried out using Matlab software. It is shown that the iterative learning method can effectively deal with this class of control problem and greatly improve the traffic response. This method can achieve an almost perfect tracking performance and eliminate the traffic jams.