Research

This project bridges different research areas including machine learning, control engineering, optimization, traffic control, and advance the state-of-the-art in distributed CAV control and traffic management field. This research aims to develop a framework for tractable modeling and optimal control of a heterogeneous traffic network consisting of autonomous and human-driven vehicles. This goal will be realized by combining data-driven modeling of uncertain systems, stochastic model predictive control, and distributed optimization.

This research focuses on controller synthesis to improve the fuel efficiency of HEVs in presence of uncertainty in future torque demand and velocity.

This research is aimed at modeling and operator specific controller design of human operated systems (such as vehicles).

This research is aimed at controller synthesis of conventional and hybrid electric vehicles to improve their fuel and energy efficiency in presence of vehicle to vehicle (V2V) and vehicle to traffic infrastructure (V2I) communication.