Linear Data Model Based Adaptive ILC for Freeway Ramp Metering Without Identical Conditions on Initial States and Reference Trajectory

Key Words:ITERATIVE LEARNING CONTROL; DISCRETE-TIME-SYSTEMS; NONLINEAR-SYSTEMS; FLOW; CONTROLLER; DESIGN

Abstract:Although freeway traffic system is conducted with a repeatable pattern day-to-day, the initial volume/or speed and the desired density of the traffic flow may vary with days due to the external disturbances. In this paper, a new linear data-model based adaptive iterative learning control (LDM-AILC) is proposed to address ramp metering in a macroscopic level freeway environment. A linear data-model is developed for the nonlinear macroscopic traffic flow model by introducing an equivalent dynamical linearization approach in the time domain. Then the LDM-AILC is designed with a feedback control law and a parameter updating law. The proposed scheme is data-driven intrinsically, where only the input and output data are required for the controller design and analysis. The convergence is shown by rigorous analysis without any identical conditions exposed on both the initial state and the reference trajectory. Extensive simulation results are provided to verify the effectiveness of the proposed LDM-AILC.

Volume:48

Issue:2

Translation or Not:no