An Iterative Learning Approach for Freeway Traffic Density Control

Xin Rong Liang; Xiao Yan Wu; Jian Ye Li

doi:10.4028/www.scientific.net/AMR.317-319.1394

Paper Titles

Study on the Intelligent Current Protection for Mining Power System
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Urban Oversaturated Traffic Network Control Based on Preference Multi-Objective Compatible Optimization Control
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Design of Air-Tight Property Detector for Gas Meter Based on C8051F350 MCU
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LPV Modeling of Buck Converter and Gain Scheduling Control
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An Iterative Learning Approach for Freeway Traffic Density Control
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The Adaptive Control of Electric Pitch Servo System
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Design of Intelligent Measure Control System for Thermal Defects
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Design and Research on the Test and Analysis System for the Proportional Relief Valve Characteristics
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The Development of an Axial Piston Pump Prototype for Seawater Desalination and its Application
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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 317-319An Iterative Learning Approach for Freeway Traffic...

An Iterative Learning Approach for Freeway Traffic Density Control

Abstract:

In this work, we apply iterative learning method to address the traffic density control problem in a macroscopic level freeway environment with ramp metering. The second-order traffic flow model is firstly formulated. Then traffic density is selected as the control variable in place of traffic occupancy. Based on the traffic flow model and in conjunction with nonlinear feedback theory, an iterative learning based traffic density controller is designed. Finally, the iterative learning based feedback controller is simulated in Matlab software. Simulation results show that this method has good dynamic and steady-state performance, and can achieve an almost perfect tracking performance.