Francesco Sorrentino
IEEE Circuits and Systems Society joint Chapter of the Vancouver/Victoria Sections

Prof. Francesco Sorrentino
Department of Mechanical Engineering
University of New Mexico

Title: Optimal Control of Networks: Energy Scaling and Open Challenges

(Presentation is available in pdf format.)

Friday, October 13, 2017, 1:30 pm to 3:00 pm
ASB 10964 (SFU's Big Data Hub), Simon Fraser University, Burnaby, BC, Canada

Light refreshments will be served.
The event is open to public.
We would greatly appreciate if you would please register so that we may more accurately estimate the room size and refreshments.
Maps: IRMACS, SFU, SFU Burnaby Campus


Joint work with I. Klickstein and A. Shirin, University of New Mexico, US

Recent years have witnessed increased interest from the scientific community regarding the control of complex dynamical networks. Some common types of networks examined throughout the literature are power grids, communication networks, gene regulatory networks, neuronal systems, food webs, and social systems. Optimal control studies strategies to control a system that minimize a cost function, for example the energy that is required by the control action.

We show that by controlling the states of a subset of the nodes of a network, rather than the state of every node, the required energy to control a portion of the network can be reduced substantially. The energy requirements exponentially decay with the number of target nodes, suggesting that large networks can be controlled by a relatively small number of inputs, as long as the target set is appropriately sized. An important observation is that the minimum energy solution of the control problem for a linear system produces a control trajectory that is nonlocal. However, when the network dynamics is linearized, the linearization is only valid in a local region of the state space and hence the question arises whether optimal control can be used. We provide a solution to this problem by determining the region of state space where the trajectory does remain local and so minimum energy control can still be applied to linearized approximations of nonlinear systems. We apply our results to develop an algorithm that determines a piecewise open-loop control signal for nonlinear systems. Applications include controlling power grid dynamics and the regulatory dynamics of the intracellular circadian clock.

[l] Shirin, Klickstein, Sorrentino, Chaos 27, 041103 (2017).
[2] Klickstein, Shirin, Sorrentino, Nature Communications, 8, 15145 (2017).
[3] Liu, Barabasi, Rev. Mod. Phys. 88, 035006 (2016).


Francesco Sorrentino received a PhD in Control Engineering from the University of Naples Federico II (Italy). He was first a postdoc and then visiting assistant professor at the Nonlinear Dynamics and Chaos group at the University of Maryland at College Park. In 2008 he became assistant professor at the University of Napoli Parthenope. In 2012 he joined the Department of Mechanical Engineering at the University of New Mexico. His research primarily focuses on cutting-edge topics in Nonlinear Dynamics and Chaos Theory. His work includes studies on dynamics and control of complex networks, adaptive sensor networks, adaptation in complex systems, optimal control, and symmetries of complex networks. Other subjects of interest are the dynamics of large networks of coupled neurons and evolutionary game theory.

Last updated 
Thu Oct 12 20:07:09 PDT 2017.