Freeway Traffic Density and On-Ramp Queue Control via ILC Approach

Abstract EN

A new queue length information fused iterative learning control approach (QLIF-ILC) is presented for freeway traffic ramp metering to achieve a better performance by utilizing the error information of the on-ramp queue length.

The QLIF-ILC consists of two parts, where the iterative feedforward part updates the control input signal by learning from the past control data in previous trials, and the current feedback part utilizes the tracking error of the current learning iteration to stabilize the controlled plant.

These two parts are combined in a complementary manner to enhance the robustness of the proposed QLIF-ILC.

A systematic approach is developed to analyze the convergence and robustness of the proposed learning scheme.

The simulation results are further given to demonstrate the effectiveness of the proposed QLIF-ILC.