Control systems and methodologies, control theory, optimization theory, biologically inspired control, robotics and robotic network control

ISR is a recognized leader in control research, one of our foundational areas of inquiry. Our faculty and students discovered new control approaches for nonlinear systems including bifurcation and control of stall scenarios for axial compressor jet engines. We emphasize numerical methods for optimization, optimization-based system design and robust control including the CONSOL and FSQP software packages implementing its algorithms. ISR developed motion description languages for robotics and have made advances in actuation and control based on signal processing. We also are advancing flocking and swarming theory, control and design for robotic groups and swarms.

Recent ISR control publications


Inferring Particle Interaction Physical Models and Their Dynamical Properties

Ion Matei, Christos Mavridis, John Baras, Maksym Zhenirovskyy

The authors propose a framework based on port-Hamiltonian modeling formalism, aimed at learning interaction models between particles and dynamical properties such as trajectory symmetries and conservation laws of ensembles (or swarms) using large-scale optimization approaches.

2019 IEEE Conference on Decision and Control

Fast, Composable Rescue Mission Planning for UAVs using Metric Temporal Logic

Usman Fiaz, John Baras

A hybrid compositional approach to time-critical search and rescue planning for quadrotor UAVs.

Report from Dagstuhl Seminar 1922: Control of Networked Cyber-Physical Systems

Edited by John Baras, Sandra Hirche, Kay Römer and Klaus Wehrle

This report documents the program and the outcomes of Dagstuhl Seminar 19222, "Control of Networked Cyber-Physical Systems" (May 26–29. 2019). In a series of impulse talks and plenary discussions, the seminar reviewed the current state of the art in CPS research and identified promising research directions that may benefit from closer cooperation between the communication and control communities.


On Distributed Solution of Ill-Conditioned System of Linear Equationsunder Communication Delays

Kushal Chakrabarti, Nirupam Gupta and Nikhil Chopra

This paper considers a distributed solution for a system of linear equations. The underlying peer-to-peer communication network is assumed to be undirected, however, the communication links are subject to potentially large but constant delays. The authors propose an algorithm that solves a distributed least-squares problem, which is equivalent to solving the system of linear equations.


Estimation of Residential Radon Concentration in Pennsylvania Counties by Data Fusion

Xuze Zhang, Saumyadipta Pyne, Benjamin Kedem

A density ratio model with an extension of variable tilts can estimate residential radon level distribution in areas where exposure to underground radon may be an issue.


Estimation of Small Tail Probabilities by Repeated Fusion

Benjamin Kedem, Lemeng Pan, Paul J. Smith. Chen Wang

The paper presents the novel statistical idea of “Down-Up” sequences which “capture” small tail probabilities with surprising precision without knowing the underlying probability distributions.

Mathematics and Statistics


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