Abstract: A wide class of interconnected systems ranging from power grid, chemical refinery plant, and water distribution system to wireless sensor network, and even swarm robotics fall in the category of cyber-physical systems. Control of such a complex networked-system has raised a number of new challenges. Conventionally, a single node or agent having access to all the information solves the control problem and manages the entire networked-system. However, with the advancement of embedded electronics and communication technology enabling local computation and low-cost communication, distributed optimization and control has gained a significant attention. In this talk, we will present two algorithms for solving distributed optimization problems in an uncertain environment. We consider a fairly general class of optimization problems being mixed-integer convex programs. In particular, we consider a network of processors aiming at cooperatively solving the uncertain optimization problem. Each node only knows a common cost function and its local uncertain constraint set. We propose a randomized, distributed algorithm working under time-varying, asynchronous and directed communication topology.
Bio: Mohammadreza Chamanbaz was born in Shiraz, Iran in 1985. In 2008 he received his BSc in Electrical Engineering from Shiraz University of Technology, Shiraz, Iran. In 2014 he received his PhD in control science from the Department of Elec- trical & Computer Engineering, National University of Singapore. He was with Data Storage Institute, Singapore as research scholar from 2010 to 2014 and with Singapore University of Technology and Design as postdoctoral research fellow from 2014 to 2017.
Mohammadreza is now Assistant Professor in Arak University of Technology, Arak, Iran. He is a member of IEEE Technical Committee on Computational Aspects of Control System Design (CACSD). His research activities are mainly focused on probabilistic and randomized algorithms for analysis and control of uncertain systems, convex optimization and robust control.