Traffic congestion control for Aw–Rascle–Zhang model. (February 2019)

• Record Type: Journal Article
• Authors: Yu, Huan; Krstic, Miroslav
• Is Part Of: Automatica. Volume 100(2019)
• Page Start: 38
• Page End: 51
• Publication Date: 2019-02
• Subjects: ARZ traffic model -- PDE control -- Backstepping -- Adaptive output feedback; Automatic control -- Periodicals; Automation -- Periodicals; 629.805
• DOI: 10.1016/j.automatica.2018.10.040
• Languages: English
• ISSNs: 0005-1098

Abstract

Abstract: This paper develops boundary feedback control laws to reduce stop-and-go oscillations in congested traffic. The macroscopic traffic dynamics are governed by Aw–Rascle–Zhang (ARZ) model, consisting of second-order nonlinear partial differential equations (PDEs). A criterion to distinguish free and congested regimes for the ARZ traffic model leads to the study of hetero-directional hyperbolic PDE model of congested traffic regime. To stabilize the oscillations of traffic density and speed in a freeway segment, a boundary input through ramp metering is considered. We discuss the stabilization problem for freeway segments respectively, upstream and downstream of the ramp. For the more challenging upstream control problem, our full-state feedback control law employs a backstepping transformation. Both collocated and anti-collocated boundary observers are designed. The exponential stability in L 2 sense and finite time convergence to equilibrium are achieved and validated with simulation. In the absence of relaxation time and boundary parameters' knowledge, we propose adaptive output feedback control design. Control is applied at outlet and the measurement is taken from inlet of the freeway segment. We use the backstepping method to obtain an observer canonical form in which unknown parameters multiply with measured output. A parametric model based on this form is derived and gradient-based parameter estimators are designed. An explicit state observer involving the delayed values of the input and the output is introduced for state estimation. Using the parameter and state estimates, we develop an adaptive output feedback control law which achieves convergence to the steady regulation in the L 2 sense.