A Feedback Linearization Approach for Coordinated Traffic Flow Management in Highway Systems

Abstract

This paper aims to develop a traffic control approach that provides an optimal real-time solution for large-scale highway networks. The traffic flow model’s nonlinearity is the main reason for the complex optimization problems that consequently require high computational effort. Feedback linearization is a well-known approach in nonlinear systems control. It can provide an exact linear representation of the original nonlinear system. Thus, it facilitates further steps in the controller design. This research combines the feedback linearization method and linear MPC to simultaneously guarantee optimal performance and real-time feasibility. While developing feedback linearization for METANET model, we discovered a pattern that describes the expansion of the control signals (ramp metering and variable speed limits) and disturbances effects through the highway networks. Utilizing this pattern, we design a generic feedback linearization control law for METANET model. The control law provides the key connection between the linearized and original model. Finally, we complete the methodology by employing a linear MPC to regulate the linearized model. The performance of the designed method is evaluated by conducting comprehensive simulation studies, including a large-scale network. The simulation results are promising. Eventually, comparing the developed methodology with an equivalent nonlinear MPC verifies a substantial improvement in computational costs.