Effects of iterative learning based signal control strategies on macroscopic fundamental diagrams of urban road networks

Abstract

Urban traffic flows are inherently repeated on a daily or weekly basis. This repeatability can help improve the traffic conditions if it is used properly by the control system. In this paper, we propose a novel iterative learning control (ILC) strategy for traffic signals of urban road networks using the repeatability feature of traffic flow. To improve the control robustness, the ILC strategy is further integrated with an error feedback control law in a complementary manner. Theoretical analysis indicates that the ILC-based traffic signal control methods can guarantee the asymptotic learning convergence, despite the presence of modeling uncertainties and exogenous disturbances. Finally, the impacts of the ILC-based signal control strategies on the network macroscopic fundamental diagram (MFD) are examined. The results show that the proposed ILC-based control strategies can homogenously distribute the network accumulation by controlling the vehicle numbers in each link to the desired levels under different traffic demands, which can result in the network with high capacity and mobility.

Improved D‐Type Anticipatory Iterative Learning Control for a Class of Inhomogeneous Heat Equations

“…However, it cannot be guaranteed by (2), and hence a relatively larger tracking error might be induced when (2) is applied. The superiority of the revised ILC law (8) to (2) has been demonstrated by simulation in Section III.…”

“…Theorem 1. Consider the PDE process (1) under the ILC law (8) and Assumption 1. Then, e i * (t), t ∈ [0, T] will converge to the ((1 + 1 ) ∕(1 − ))-neighborhood of zero asymptotically in the sense of -norm as i → ∞, where = 4ū ∫ 0 (1, − )d , 1 = 2| | sup t∈[0,T] | (1, t)|, with u being the input saturation bound.…”

“…Further simulate the ILC of the heat transfer system via (8). For fair comparison, we still choose = 0.25 min.…”

“…Summarizing them, one trend is to focus on ILC algorithm design for more general problem settings, e.g., iteration-varying trial lengths [2], disturbances with high-order internal model [3], stochastic factors [4], as well as high-dimensional or even infinite-dimensional process model [1,5]. Another trend is to apply the well-established ILC theory to all kinds of industrial or engineering processes, e.g., multi-phase batch processes [6], robotic fish [7], urban road networks [8], marine vibrator [9], wind turbines [10], permanent-magnet spherical actuator [11], and high-speed train [12].…”

Improved Road‐Network‐Flow Control Strategy Based on Macroscopic Fundamental Diagrams and Queuing Length in Connected‐Vehicle Network

“…For example, Yan et al [11] proposed a novel iterative learning control (ILC) strategy for traffic signals of urban road networks using the repeatability feature of traffic flow. Zhong et al [12] explored a new robust perimeter control framework for dynamic traffic networks with parameter uncertainty (on the MFD) and exogenous disturbance induced by travel demand.…”