Networks are ubiquitous and often at the heart of fundamental research in science and engineering, from the Internet, to social networks, bio-chemical reaction networks, transportation networks, power networks, and pharmacology networks that analyze chemical and clinical properties of drug-like molecules. In this talk, I will present some recently developed methods on analyzing such complex network systems, particularly from the point of view of optimization and control theory. I will divide my talk into two parts: In the first part of the talk, I will describe novel fixed-time convergent algorithms for convex optimization and its application to distributed optimization, and variational inequality problems. Many practical applications, such as economic dispatch in power systems, often undergo frequent and severe changes in operating conditions, and thus require fast solutions irrespective of the initial conditions. Tools from fixed-time Lyapunov theory are leveraged for achieving global fixed-time convergence guarantees. In the second part of the talk, I will look into designing control algorithms that scale efficiently from single agent to multiple agents. Communication agnostic control framework for microgrid systems will be of interest. Dynamical similarity among multiple agents (power converters) is exploited to model the entire microgrid as an equivalent single converter system. Moreover, various performance objectives, such as, voltage regulation and robust stability of the overall microgrid system can be analyzed in terms of the performance objectives of the single converter system. At the end, I will give a brief overview of the other ongoing projects, pointing out several future directions.
Mayank Baranwal is a postdoctoral scholar in the Department of Electrical Engineering and Computer Science at the University of Michigan, Ann Arbor. He obtained his Bachelors in Mechanical Engineering in 2011 from Indian Institute of Technology, Kanpur, and MS in Mechanical Science and Engineering in 2014, MS in Mathematics in 2015 and PhD in Mechanical Science and Engineering in 2018, all from the University of Illinois at Urbana-Champaign. His research interests are in modeling, optimization, control and inference in network systems with applications to distributed optimization, reduction of biochemical networks, transportation networks and control of microgrids.