Control, Optimization and Modeling

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

ISR is a recognized leader in control, optimization and modeling, foundational to our research. 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 publications

2020

Reaction Network Analysis for Atomic Layer Deposition Processes

Raymond Adomaitis

Modeling the dynamics of atomic layer deposition processes is challenging because of the nonlinear behavior of these systems, their multiple and widely-ranging timescales, and by the relative lack of validated reaction kinetics information. Those data that do exist are typically derived from quantum chemical computations or experimental examinations of reaction sequences that define only a portion of the complete ALD reaction cycle. In this talk, I will describe our efforts to develop mathematical methods that address the numerical challenge of simulating dynamic ALD processes while providing a rational path to creating well-posed models of these deposition processes. Our model reduction approach is based on a two-step procedure where in the first step, the chemical species surface balance dynamic equations are factored to decouple the (nonlinear) reaction rates, eliminating redundant dynamic modes. The second phase further reduces the dynamic dimension when species relatively minor in concentration can be identified. The overall technique extracts physically significant reaction invariants and points to potential model structural problems if they exist.

Invited Paper, 73rd Annual Gaseous Electronics Virtual Conference of the American Physical Society, Oct. 5, 2020

2020

Delay-sensitive Joint Optimal Control and Resource Management in Multi-loop Networked Control Systems

Mohammad H. Mamduhi, Dipankar Maity, Sandra Hirche, John S.Baras, Karl H. Johansson

Proposes a cross-layer optimal co-design of control, sampling and resource management policies for a networked control system consisting of multiple stochastic linear time-invariant systems which close their sensor-to-controller loops over a shared network.

arXiv.org preprint (to appear in the IEEE Transactions on Control of Network Systems)

Learning physical laws: the case of micron size particles in dielectric fluid

Ion Matei, Maksym Zhenirovskyy, Johan de Kleer, Christoforos Somarakis, John Baras

Addresses the problem of learning laws governing the behavior of physical systems.

2020 American Control Conference

Metric Interval Temporal Logic based Reinforcement Learning with Runtime Monitoring and Self-Correction

Zhenyu Lin, John Baras

A modular Q-learning framework to deal with the robot task planning, runtime monitoring and self-correction problem.

2020 American Control Conference

Stable Consensus Decision Making for Spatially Distributed Multiagent Systems with Multiple Leaders

Zhixin Liu, Lin Wang, Daoyi Dong, John Baras

This paper considers the consensus decision-making problem of spatially distributed multiagent systems with multiple leaders, where the leaders have the preference about the destination, while the followers have no such preference.

SIAM Journal on Control and Optimization

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

2019

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.

arXiv.org

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.

2020

Bilateral Teleoperation of Soft Robots under Piecewise Constant Curvature Hypothesis: An Experimental Investigation

Lasitha Weerakoon, Nikhil Chopra

This paper investigates an adaptive task space bilateral teleoperation framework for soft robots with dynamic uncertainties. It assumes a non-redundant rigid master manipulator and a redundant soft slave manipulator under the piecewise constant curvature hypothesis.

2020 American Control Conference

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.

arXiv.org

2019

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.

2020

Computing Sensitivities for Distortion Risk Measures

Peter Glynn, Yijie Peng, Michael Fu, Jian-Qiang Hu

Proposes a new sensitivity estimator for the distortion risk measure that uses the generalized likelihood ratio estimators in Peng et al.(2020) for distribution sensitivities as input and establish a central limit theorem for the new estimator. The proposed estimator can handle discontinuous sample paths and distortion functions.

researchgate.net (Submitted to INFORMS Journal on Computing)

Dynamic estimation of auditory temporal response functions via state-space models with Gaussian mixture process noise

Sina Miran, Behtash Babadi, Alessandro Presacco, Jonathan Simon, Michael Fu, Steven Marcus

This research develops efficient algorithms for inferring the parameters of a general class of Gaussian mixture process noise models from noisy and limited observations, and utilize them in extracting the neural dynamics that underlie auditory processing from magnetoencephalography (MEG) data in a cocktail party setting.

PLOS Computational Biology

Option Pricing Under a Discrete-Time Markov Switching Stochastic Volatility with Co-Jump Mode

Michael C. Fu, Bingqing Li, Rongwen Wu, Tianqi Zhang

Considers option pricing using a discrete-time Markov switching stochastic volatility with co-jump model, which can model volatility clustering and varying mean-reversion speeds of volatility. For pricing European options, the authors develop a computationally efficient method for obtaining the probability distribution of average integrated variance (AIV), which is key to option pricing under stochastic-volatility-type models. Building upon the efficiency of the European option pricing approach, they are able to price an American-style option, by converting its pricing into the pricing of a portfolio of European options. The work also provides constructive guidance for analyzing derivatives based on variance, e.g., the variance swap.

arXiv.org

Predictive Modeling for Epidemic Outbreaks

Jian Chen, Michael C. Fu, Wenhong Zhang, Junhua Zheng

New modeling research is helping decisionmakers better forecast the spread of the COVID-19 pandemic. The model has been adopted by the Shanghai assistance medical team in Wuhan’s Jinyintan Hospital, the first designated hospital in the world to take COVID-19 patients. Forecasts from the new model have been used to prepare medical staff, intensive care unit beds, ventilators, and other critical care medical resources, as well as to support real-time medical management decisions.

Asia-Pacific Journal of Operational Research

2019

Random directions stochastic approximation with deterministic perturbations

L.A. Prashanth, Shalabh Bhatnagar, Nirav Bhavsar, Michael C. Fu, Steven Marcus

Paper introduces deterministic perturbation schemes for random directions stochastic approximation.

IEEE Transactions on Automatic Control

2020

Independent Vector Analysis for Molecular Data Fusion: Application to Property Prediction and Knowledge Discovery of Energetic Materials

Zois Boukouvalas, Monica Puerto, Daniel Elton, Peter Chung, Mark Fuge

Proposes a data fusion framework that uses Independent Vector Analysis to uncover underlying complementary information contained in different molecular featurization methods.

EUSIPCO 2020

Design and Validation of a Method to Characterize Human Interaction Variability

Kailyn Cage, Monifa Vaughn-Cooke, Mark Fuge

Introduces and validate the effectiveness of the Interaction Variability method, which maps product components and musculoskeletal regions to determine explicit design parameters through limiting designer variation in the classification of human interaction factors.

MDPI Systems: Special Issue on Human Factors in Systems Engineering

Learning to Abstract and Compose Mechanical Device Function and Behavior

Jun Wang, Kevin Chiu, Mark Fuge

Investigates the joint use of Physics-Informed (Navier-Stokes equations) Deep Neural Networks (i.e.,Deconvolutional Neural Networks) as well as Geometric Deep Learning (i.e., Graph Neural Networks) to learn and compose fluid component behavior. The models successfully predict the fluid flows and their composition behaviors (i.e., velocity fields) with an accuracy of about 99%.

researchgate.net

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 improving the representation compactness while maintaining sufficient representation capacity.

arXiv.org

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2020

Risk-Based, Multistage Stochastic Energy Project Selection

Billy Champion, Steven Gabriel, Ahti Salo

A risk-based, stochastic multi-period model with binary decision variables at each stage has usefulness for planning the retrofitting of buildings for energy conservation.

Energy Systems

2020

Estimation of Tail Probabilities by Repeated Augmented Reality

Benjamin Kedem, Saumyadipta Pyne

This article advances the notion of repeated augmented reality in the estimation of very small tail probabilities even from moderately sized samples. Synthetic data can enhance patterns in real data and thus provide insights into different phenomena. Here, the estimation of tail probabilities of rare events from a moderately large number of observations is considered. The problem is approached by a large number of augmentations or fusions of the real data with computer-generated synthetic samples. The tail probability of interest is approximated by subsequences created by a novel iterative process. The estimates are found to be quite precise. Residential radon level data from Beaver County, Pa., is used as an illustration.

healthanalytics.net

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.

arXiv.org

2019

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

 

2020

Dynamic estimation of auditory temporal response functions via state-space models with Gaussian mixture process noise

Sina Miran, Behtash Babadi, Alessandro Presacco, Jonathan Simon, Michael Fu, Steven Marcus

This research develops efficient algorithms for inferring the parameters of a general class of Gaussian mixture process noise models from noisy and limited observations, and utilize them in extracting the neural dynamics that underlie auditory processing from magnetoencephalography (MEG) data in a cocktail party setting.

PLOS Computational Biology

2019

Random directions stochastic approximation with deterministic perturbations

L.A. Prashanth, Shalabh Bhatnagar, Nirav Bhavsar, Michael C. Fu, Steven Marcus

Paper introduces deterministic perturbation schemes for random directions stochastic approximation.

IEEE Transactions on Automatic Control

2020

Non-Gaussian Estimation and Dynamic Output Feedback Using the Gaussian Mixture Kalman Filter

Debdipta Goswami, Derek Paley

Considers the problem of non-Gaussian estimation and dynamic output feedback in both linear and nonlinear settings. Estimation with non-Gaussian process noise, although important in fields such as environmental sampling, is typically problem specific and suboptimal. The approach described here uses the Gaussian mixture model to approximate an unknown non-Gaussian distribution and to employ the Kalman filter and its nonlinear variants: the extended and unscented Kalman filters.

Journal of Guidance, Control and Dynamics

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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