Control and Optimization

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

ISR is a recognized leader in control and optimization 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 publications


A Cross-layer Optimal Co-design of Control and Networking in Time-sensitive Cyber-Physical Systems

Mohammad Mamduhi, Dipankar Maity, John Baras, Karl Johansson

In the design of cyber-physical systems (CPS) where multiple heterogeneous physical systems are coupled via a communication network, a key aspect is to study how network services are distributed among the users. The authors derive the joint optimal time-sensitive control and service allocation policies for each physical system.

KTH Royal Institute of Technology, Stockholm


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.


Preserving Statistical Privacy in Distributed Optimization

Nirupam Gupta, Shripad Gaid, Nikhil Chopra, Nitin Vaidya

This paper propose a distributed optimization algorithm that, additionally, preserves statistical privacy of agents’ cost functions against a passive adversary that corrupts some agents in the network. The algorithm is a composition of a distributed “zero-sum” secret sharing protocol that obfuscates the agents’ cost functions, and a standard non-private distributed optimization method.


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.


Airfoil Design Parameterization and Optimization using Bézier Generative Adversarial Networks

Wei Chen, Kevin Chiu, Mark Fuge

The authors propose a deep generative model, Bézier-GAN,to parameterize aerodynamic designs by learning from shape variations in an existing database. The resulted new parameterization can accelerate design optimization convergence by improv-ing the representation compactness while maintaining sufficient representation capacity.


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



Optimal control of a 1D diffusion process with a team of mobile actuators under jointly optimal guidance

Sheng Cheng, Derek Paley

An optimization framework to control a distributed parameter system (DPS) using a team of mobile actuators. The optimization simultaneously seeks efficient guidance of the mobile actuators and effective control of the DPS such that an integrated cost function associated with both the mobile actuators and the DPS is minimized.

CDCL paper

Bioinspired pursuit with a swimming robot using feedback control of an internal rotor

Brian Free, Jinseong Lee, Derek Paley

The paper presents a state-feedback control law for steering a fish-inspired robot in a desired direction, and engaging it in pure pursuit.

Bioinspiration and Biomimetics

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