Data preconditioning for data-driven predictive control in urban traffic control
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Date
2024-10-07
Publication Type
Master Thesis
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Abstract
Traffic congestion in urban areas presents a significant challenge, often without the possibility of expanding road infrastructure. To address this, we employ Data-EnablEd Predictive Control (DeePC) to dynamically adjust the green-red cycles of traffic lights based on current traffic conditions. Given the nonlinear nature of urban traffic networks, we propose modeling them as Linear Parameter-Varying (LPV) systems.
Our research demonstrates how to adapt the data used by DeePC for decision-making, contingent on known switching times of the LPV system. We establish the equivalence of closed-loop behavior between Model-Predictive Control (MPC) and DeePC during specific intervals of the LPV system. Offline data clustering is utilized to group traffic data from various days into behavior groups, which represent the subsystems of our urban traffic network modeled as an LPV system. The most appropriate data cluster is selected online to match current traffic conditions, and the controller’s data is updated online as the urban traffic network transitions between different behaviors when we control the urban traffic network with our proposed data-switching DeePC algorithm.
Numerical simulations illustrate that the proposed algorithm enhances the robustness of traffic congestion prevention and improves computational efficiency compared to a DeePC controller with fixed data.
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Examiner : Rimoldi, Alessio
Examiner : Cenedese, Carlo
Examiner: Padoan, Alberto
Examiner: Lygeros, John
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ETH Zurich
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Subject
smart mobility; DeePC; Urban traffic control; LPV systems; Data clustering
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03751 - Lygeros, John / Lygeros, John