Self-Optimizing Traffic Light Control Using Hybrid Accelerated Extremum Seeking

Abstract

Motivated by the shallow concavity properties that emerge in certain response maps in the context of optimization problems in transportation systems, we study the stability properties of a class of hybrid accelerated extremum seeking (HAES) dynamics interconnected with dynamic plants in the loop. In particular, we establish suitable semi-global practical asymptotic stability properties for different classes of cost functions, as well as tuning conditions for the hybrid extremum seeking algorithm. Additionally, we implement the HAES to optimize the performance of a self-organizing traffic light system (SOTL) in a class of smart transportation systems. We show that the dynamic momentum mechanism incorporated by the HAES can significantly reduce the convergence time in the optimization process compared to the traditional extremum seeking algorithms based on gradient descent flows.