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 news

Recent publications

2021

Energy modeling and control for improved engine stability and efficiency in air vehicles

Rupamathi Jaddivada (MIT), Marija Ilic (MIT), Eyad Abed

The researchers explore the potential of active control for end-to-end power trains of hybrid electric aircraft. They expand the operating envelope beyond compressor stall and surge limits, allowing a smaller engine to attain the same performance as a larger one.

Annual Reviews in Control 52

2019

Local modal participation analysis of nonlinear systems using Poincaré linearization

Boumediene Hamz (Imperial College London) and Eyad Abed

The paper studies an extension to nonlinear systems of a recently proposed approach to the definition of modal participation factors.

Nonlinear Dynamics, Nov. 26, 2019

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

2021

Non-Asymptotic Guarantees for Robust Identification of Granger Causality via the LASSO

Proloy Das, Behtash Babadi

Classical statistical tests for Granger causality resort to asymptotic analysis of ordinary least squares, which require long data durations to be useful and are not immune to confounding effects. In this work, we close this gap by introducing a LASSO-based statistic and studying its non-asymptotic properties under the assumption that the true models admit sparse autoregressive representations. We establish that the sufficient conditions of LASSO also suffice for robust identification of Granger causal influences. We also characterize the false positive error probability of a simple thresholding rule for identifying Granger causal effects. We present simulation studies and application to real data to compare the performance of the ordinary least squares and LASSO in detecting Granger causal influences, which corroborate our theoretical results.

arXiv.org

2022

Cooperative Multi-Lane Shock Wave Detection and Dissipation via Local Communication

Nilesh Suriyarachchi, Christos Mavridis, John Baras

Traffic shock waves are well-known naturally occurring phenomena that lead to unnecessary congestion in highway networks. Introducing connected autonomous vehicles (CAVs) to highways of human-driven vehicles (HDVs) allows for the development of traffic control schemes that can mitigate the effects of the shock waves. The authors propose a shock wave detection algorithm based on communication between CAVs with local traffic information. The methodology is suitable for multi-lane mixed traffic highways of arbitrary structure, i.e., it is not limited to closed-circuit ring roads. The researchers show that the detection information can be used to design a class of proactive shock wave mitigating CAV controllers. The choice of the controller can depend on design parameters such as the aggressiveness of the driving behavior allowed.

2022 30th Mediterranean Conference on Control and Automation (MED)

PWA-CTM: An Extended Cell-Transmission Model based on Piecewise Affine Approximation of the Fundamental Diagram

Fatemeh Alimardani, John Baras

Throughout the past decades, many different versions of the widely used first-order Cell-Transmission Model (CTM) have been proposed for optimal traffic control. Highway traffic management techniques such as Ramp Metering (RM) are typically designed based on an optimization problem with nonlinear constraints originating in the flow-density relation of the Fundamental Diagram (FD). Most of the extended CTM versions are based on the trapezoidal approximation of the flow-density relation of the Fundamental Diagram (FD) in an attempt to simplify the optimization problem. However, this relation is naturally nonlinear, and crude approximations can greatly impact the efficiency of the optimization solution. The authors propose a class of extended CTMs that are based on piecewise affine approximations of the flow-density relation such that (a) the integrated squared error with respect to the true relation is greatly reduced in comparison to the trapezoidal approximation, and (b) the optimization problem remains tractable for real-time application of ramp metering optimal controllers.

2022 30th Mediterranean Conference on Control and Automation (MED)

Sparse Gaussian Process Regression using Progressively Growing Learning Representations

Christos Mavridis, George Kontudis, John Baras

Here, the authors introduce a sparse Gaussian process regression model whose covariance function is parameterized by the locations of a progressively growing set of pseudo-inputs generated by an online deterministic annealing optimization algorithm. This is an active learning approach, which, in contrast to most existing works, can modify already selected pseudo-inputs and is trained with recursive, gradient-free updates.

61st IEEE Conference on Decision and Control (2022)

Actuator Scheduling for Linear Systems: A Convex Relaxation Approach

Junjie Jiao, Dipankar Maity, John Baras, Sandra Hirche

Investigates the problem of actuator scheduling for networked control systems. Given a stochastic linear system with a number of actuators, the authors consider the case that one actuator is activated at each time. This problem is combinatorial in nature and NP hard to solve. They propose a convex relaxation to the actuator scheduling problem, and use its solution as a reference to design an algorithm for solving the original scheduling problem. Using dynamic programming arguments, they provide a suboptimality bound of a proposed algorithm.

arXiv.org

Maximum-Entropy Progressive State Aggregation for Reinforcement Learning

Christos Mavridis, Nilesh Suriyarachchi, John Baras

A reinforcement learning algorithm based on an adaptive state aggregation scheme defined by a progressively growing set of codevectors placed in the joint state-action space according to a maximum-entropy vector quantization scheme.

2021 60th IEEE Conference on Decision and Control (CDC)

Micro-scale chiplet assembly control with chiplet-to-chiplet potential interaction

Ion Matei, Anna Plochowietz, Johan de Kleer, John Baras

The authors address the problem of simultaneous control of micro-objects (chiplets) immersed in dielectric fluid. An electric field, shaped by an array of thousands of electrodes, is used to transport and position chiplets using dielectrophoretic forces. They use a lumped, 2D, capacitive based (nonlinear) model of motion for the chiplets' behavior that include chiplet to chiplet interactions. The chiplet positions are tracked using a high speed camera and image processing algorithms. They use a model predictive control (MPC) approach to derive control inputs (i.e., electrode potentials) that shape the chiplets into a desired pattern.

2021 60th IEEE Conference on Decision and Control (CDC)

Progressive Graph Partitioning Based on Information Diffusion

Christos Mavridis, John Baras

Proposes an online deterministic annealing algorithm for progressive graph partitioning based on the spectral information of the underlying graph Laplacian matrix.

2021 60th IEEE Conference on Decision and Control (CDC)

Risk-sensitive REINFORCE: A Monte Carlo Policy Gradient Algorithm for Exponential Performance Criteria

Erfaun Noorani, John Baras

The authors present a policy gradient theorem for the Risk-sensitive Control "exponential of integral" criteria, and propose a risk-sensitive Monte Carlo policy gradient algorithm. Simulations, together with our theoretical analysis, show that the use of the exponential criteria with an appropriately chosen risk parameter not only results in a risk-sensitive policy, but also reduces variance during learning process and accelerates learning, which in turn results in a policy with higher expected return: risk-sensitiveness leads to sample efficiency and improved performance.

2021 60th IEEE Conference on Decision and Control (CDC)

Risk-sensitive Reinforcement Learning and Robust Learning for Control

Erfaun Noorani, John Baras

Describes new foundations for robust reinforcement learning for control, by exploring analytically the relation between the KL-regularized Reinforcement Learning and the Risk-sensitive Control "exponential of integral" criteria.

2021 60th IEEE Conference on Decision and Control (CDC)

2021

Shock Wave Mitigation in Multi-lane Highways using Vehicle-to-Vehicle Communication

Nilesh Suriyarachchi, John Baras

The communication and sensing capabilities of modern connected autonomous vehicles (CAVs) will allow new approaches in control to help solve the problem of stop-and-go waves in highway networks. The paper introduces a communication-based cooperative control method for CAVs in multi-lane highways in a mixed traffic setting. Each vehicle is able to take proactive control actions. This is an improvement over existing reactive methods which rely on shock waves already being present. In addition, the new method’s performance is independent of the highway structure; the algorithm performs identically on ring roads like “beltways” and straight roads. The method allows for proactive control application and exhibits good shock wave dissipation performance even when only a few CAVs are present amongst conventional vehicles. The results were verified on a three-lane circular highway loop using realistic traffic simulation software.

IEEE 94th Vehicular Technology Conference (Fall 2021)

A Robust Mean-Field Game of Boltzmann-Vlasov-like Traffic Flow

Amoolya Tirumalai, John Baras

Introduces a particle-based model of autonomous vehicles subject to drag and unknown disturbances, noise, and a speed limit in addition to the control. The authors formulate a robust stochastic differential game on the particles and pass formally to the infinite-particle limit to obtain a robust mean-field game PDE system. They solve the mean-field game PDE system numerically and obtain an optimal control which increases the bulk velocity of the traffic flow while reducing congestion.

arXiv.org

Value of Information in Feedback Control: Quantification

Touraj Soleymani, John Baras, Sandra Hirche

Even though transmission of a piece of sensory information in a networked control system decreases the uncertainty of the controller, it indeed has a price from the economic perspective. It is, therefore, rational that such information be transmitted only if it is valuable in the sense of a cost-benefit analysis, i.e., only if its benefit surpasses its cost. Yet, the fact is that little is known so far about this valuation of information. The authors in this paper study an essential property of controlled stochastic processes by making a rate-regulation trade-off defined between the packet rate and regulation cost. They show that the valuation of information is conceivable and quantifiable grounded on this trade-off. They appeal to a game-theoretic analysis, and seek equilibria at which neither decision maker has a unilateral incentive to change its policy.

IEEE Transactions on Automatic Control

Vector Quantization for Adaptive State Aggregation in Reinforcement Learning

Christos Mavridis, John Baras

The authors propose an adaptive state aggregation scheme to be used along with temporal-difference reinforcement learning and value function approximation algorithms. The resulting algorithm constitutes a two-timescale stochastic approximation algorithm with: (a) a fast component that executes a temporal-difference reinforcement learning algorithm, and (b) a slow component, based on online vector quantization, that adaptively partitions the state space of a Markov Decision Process according to an appropriately defined dissimilarity measure.

2021 American Control Conference

Feedback Control over Noisy Channels: Characterization of a General Equilibrium

Touraj Soleymani, John Baras, Sandra Hirche, Karl Johansson

A study of an energy-regulation trade-off that delineates the fundamental performance bound of a feedback control system over a noisy channel in an unreliable communication regime.

IEEE Transactions on Automatic Control

Value of Information in Feedback Control: Global Optimality

Touraj Soleymani, John Baras, Sandra Hirche, Karl Johansson

The rate-regulation trade-off defined between two objective functions, one penalizing the packet rate and the other, the state deviation and control effort, can express the performance bound of a networked control system. However, the characterization of the set of globally optimal solutions in this trade-off for multi-dimensional controlled Gauss-Markov processes has been an open problem. In the present article, we characterize a policy profile that belongs to this set. We prove that such a policy profile consists of a symmetric threshold triggering policy, which can be expressed in terms of the value of information, and a certainty-equivalent control policy, which uses a conditional expectation with linear dynamics.

arXiv.org

Learning Interaction Dynamics from Particle Trajectories and Density Evolution

Christos N. Mavridis, Amoolya Tirumalai, John Baras

This paper proposes a family of parametric interaction functions in the general Cucker-Smale model such that the mean-field macroscopic system of equations can be iteratively solved in an optimization scheme aiming to learn the inter-action dynamics of the microscopic model from observations of macroscopic quantities.

59th IEEE Conference on Decision and Control

Dimensionality Reduction of Volterra Kernels by Tensor Decomposition using Higher-Order SVD

Urszula Libal, John Baras, Karl Johansson

The paper proposes a practical method for a significant dimensionality reduction of Volterra kernels, defining a discrete nonlinear model of a signal by Volterra series of higher order.

59th IEEE Conference on Decision and Control

Value of Information in Feedback Control: Global Optimality

Touraj Soleymani, John Baras, Sandra Hirche, Karl Johansson

The rate-regulation trade-off defined between two objective functions, one penalizing the packet rate and one the state deviation and control effort, can express the performance bound of a networked control system. However, the characterization of the set of globally optimal solutions in this trade-off for multi-dimensional controlled Gauss-Markov processes has been an open problem. IThis article characterizes a policyprofile that belongs to this set.

arXiv.org

2020

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

Mohammad Mamduhi, Dipankar Maity, Sandra Hirche, John Baras, Karl 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.

2022

Passivity-Based Control of Robots: Theory and Examples from the Literature

Nikhil Chopra, Masayuki Fujita, Romeo Ortega, Mark Spong

The use of passivity in control theory was motivated by the earlier use of passivity in circuit theory and passive network synthesis, beginning in the 1950s. With the well-known analogy between electrical and mechanical systems, it is natural that passivity should play an important role in the control of mechanical systems and, in particular, in the control of robots. This article provides a historical overview of passivity-based robot control.

IEEE Control Systems Magazine

On Pre-Conditioning of Decentralized Gradient-Descent when Solving a System of Linear Equations

Kushal Chakrabarti, Nirupam Gupta, Nikhil Chopra

Considers solving an overdetermined system of linear equations in peer-to-peer multi-agent networks. The network is assumed to be synchronous and (strongly) connected.

IEEE Transactions on Control of Network Systems

Iterative pre-conditioning for expediting the distributed gradient-descent method: The case of linear least-squares problem

Kushal Chakrabarti, Nirupam Gupta, Nikhil Chopra

This paper considers the multi-agent linear least-squares problem in a server–agent network architecture. The authors propose an iterative pre-conditioning technique to mitigate the deleterious impact of the data points’ conditioning on the convergence rate of the gradient-descent method.

Automatica

2021

On Accelerating Distributed Convex Optimizations

Kushal Chakrabarti, Nirupam Gupta, Nikhil Chopra

Studies a distributed multi-agent convex optimization problem. The system comprises multiple agents in this problem, each with a set of local data points and an associated local cost function.

arXiv.org

Robustness of Iteratively Pre-Conditioned Gradient-Descent Method: The Case of Distributed Linear Regression Problem

Kushal Chakrabarti, Nirupam Gupta, Nikhil Chopra

This paper considers the problem of multi-agent distributed linear regression in the presence of system noises. The authors empirically show that the robustness of the Iteratively Pre-conditioned Gradient-descent (IPG) method compares favorably to state-of-the-art algorithms.

2021 American Control Conference

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

2022

Assessing the trade-off between prediction accuracy and interpretability for topic modeling on energetic materials corpora

Monica Puerto, Mason Kellett, Rodanthi Nikopoulou, Mark Fuge, Ruth Doherty, Peter Chung, and Zois Boukouvalas

As the amount and variety of energetics research increases, machine aware topic identification is necessary to streamline future research pipelines. The makeup of an automatic topic identification process consists of creating document representations and performing classification. However, the implementation of these processes on energetics research imposes new challenges. Energetics datasets contain many scientific terms that are necessary to understand the context of a document but may require more complex document representations. Secondly, the predictions from classification must be understandable and trusted by the chemists within the pipeline. In this work, the authors study the trade-off between prediction accuracy and interpretability by implementing three document embedding methods that vary in computational complexity.

arXiv.org

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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2022

A New Computational Approach for Solving Linear Bilevel Programs Based on Parameter-Free Disjunctive Decomposition

Saeed Mohammadi, Mohammad Reza Hesamzadeh, Steven Gabriel, Dina Khastieva

Linear bilevel programs have been widely used in computational mathematics and optimization in several applications. Single-level reformulation for linear BLPs replaces the lower-level linear program with its Karush-Kuhn-Tucker optimality conditions and linearizes the complementary slackness conditions using the big-M technique. Although the approach is straightforward, it requires finding the big-M whose computation is recently shown to be NP-hard. This paper presents a disjunctive-based decomposition algorithm which does not need finding the big-Ms whereas guaranteeing that obtained solution is optimal.

arXiv.org

2020

On Linear Bilevel Optimization Problems with Complementarity-Constrained Lower Levels

Steven Gabriel, Marina Leal, Martin Schmidt

The authors consider a novel class of linear bilevel optimization models with a lower level that is a linear optimization problem with complementarity constraints. They present different single-level reformulations depending on whether the linear complementarity problem (LCP) as part of the lower-level constraint set depends on the upper-level decisions or not as well as on whether the LCP matrix is positive definite or positive semidefinite. The connection to linear trilevel models that can be seen as a special case of the considered class of bilevel problems under some additional assumptions is illustrated. Two generic and illustrative bilevel models from the fields of transportation and energy are presented to show the practical relevance of the newly introduced class of bilevel problems and show related theoretical results.

Universität Trier

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

2021

Augmented Reality: A Computational Framework Applied to Modeling the Dynamics of Air Pollution

Saumyadipta Pyne, Ryan Stauffer, Benjamin Kedem

The authors recently developed a new Augmented Reality (AR) framework to combine real data with computer-generated synthetic samples to “look under the hood”, as it were, for gaining insights into rare, dynamic phenomena. Using data fusion and density ratio model, AR allows them to estimate the tail probabilities of exceeding large thresholds that are far beyond the limited range of observations in moderately sized data. Such thresholds represent extreme events. This study models the drastic change in air pollution levels in Washington, D.C. caused by the COVID-19 pandemic lockdown in 2020.

23rd Annual Conference of the Society of Statistics, Computer and Applications

On the Probabilities of Environmental Extremes

Benjamin Kedem, Ryan Stauffer, Xuze Zhang, Saumyadipta Pyne

Environmental researchers, as well as epidemiologists, often encounter the problem of determining the probability of exceeding a high threshold of a variable of interest based on observations that are much smaller than the threshold. Moreover, the data available for that task may only be of moderate size. This generic problem is addressed by repeatedly fusing the real data numerous times with synthetic computer-generated samples. The threshold probability of interest is approximated by certain subsequences created by an iterative algorithm that gives precise estimates. The method is illustrated using environmental data including monitoring data of nitrogen dioxide levels in the air.

International Journal of Statistics in Medical Research

Extended residual coherence with a financial application

Xuze Zhang, Benjamin Kedem

Residual coherence is a graphical tool for selecting potential second-order interaction terms as functions of a single time series and its lags. This paper extends the notion of residual coherence to account for interaction terms of multiple time series. Moreover, an alternative criterion, integrated spectrum, is proposed to facilitate this graphical selection. A financial market application shows that new insights can be gained regarding implied market volatility.

Statistics in Transition

Multivariate Tail Probabilities: Predicting Regional Pertussis Cases in Washington State

Xuze Zhang, Saumyadipta Pyne, Benjamin Kedem

In disease modeling, a key statistical problem is the estimation of lower and upper tail probabilities of health events from given data sets of small size and limited range. Assuming such constraints, we describe a computational framework for the systematic fusion of observations from multiple sources to compute tail probabilities that could not be obtained otherwise due to a lack of lower or upper tail data. Regional prediction, in Washington state, of the number of pertussis cases is approached by providing joint probabilities using fused data from several relatively small samples following the selected density ratio model. The model is validated by a graphical goodness-of-fit plot comparing the estimated reference distribution obtained from the fused data with that of the empirical distribution obtained from the reference sample only.

Entropy

Financial Application of Extended Residual Coherence

Xuze Zhang, Benjamin Kedem

Residual coherence is a graphical tool for selecting potential second-order interaction terms as functions of a single time series and its lags. This paper extends the notion of residual coherence to account for interaction terms of multiple time series. Moreover, an alternative criterion, integrated spectrum,is proposed to facilitate this graphical selection.A financial market application shows that new insights can be gained regarding implied market volatility.

arXiv.org

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

 

2022

Epidemic Population Games And Evolutionary Dynamics

Nuno Martins, Jair Certório, Richard La

The authors put forth a system theoretic methodology to model and regulate the endemic prevalence of infections for the case where the decisions of a population of strategically interacting agents determine the epidemic transmission rate. In their framework, the agents choose from a finite set of strategies that influence the transmission rate, and they can repeatedly revise their choices to benefit from the strategies’ net rewards (payoff) resulting from incentives after deducting the strategies’ intrinsic costs.

arXiv.org

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

2022

Epidemic Population Games And Evolutionary Dynamics

Nuno Martins, Jair Certório, Richard La

The authors put forth a system theoretic methodology to model and regulate the endemic prevalence of infections for the case where the decisions of a population of strategically interacting agents determine the epidemic transmission rate. In their framework, the agents choose from a finite set of strategies that influence the transmission rate, and they can repeatedly revise their choices to benefit from the strategies’ net rewards (payoff) resulting from incentives after deducting the strategies’ intrinsic costs.

arXiv.org

2020

Controller Synthesis Subject to Logical and Structural Constraints: A Satisfiability Modulo Theories (SMT) Approach

MirSaleh Bahavarnia, Yasser Shoukry, Nuno Martins

A simple approach to use satisfiability modulo theories (SMT) solvers to synthesize stabilizing controllers subject to logical and structural constraints—such as the transitive property of the connectivity of a networked system, and the mutually exclusive use of inputs or sensors.

2020 American Control Conference

2022

Dynamics and Stability Analysis of a Tethered Unmanned Rotorcraft

Alexander Donkels, Johann Dauer, Derek Paley

An analysis of the dynamics and stability of a nonlinear model of a small-scale tethered rotorcraft. The authors develop a simplified model of a rotorcraft’s longitudinal dynamics and vary model parameters including tether force, trim conditions, and the horizontal wind to study the interdependence of those parameters and their impact on the model’s equilibrium points and stability. This is a preliminary step towards design of an automatic control of an unmanned rotorcraft capable of autonomous tethered landing and development of tether force control laws for the winch device.

CDCL paper

Dynamic Modeling and Simulation of Electric Scooter Interactions With a Pedestrian Crowd Using a Social Force Model

Yen-Chen Liu, Alireza Jafari, Jae Kun Shim, Derek Paley

This study presents a modified social force model to predict the interactions of an electric scooter with a pedestrian crowd by considering the scooter's kinematics constraints and geometry and the velocity-dependent behaviors of the rider.

IEEE Transactions on Intelligent Transportation

Multi-Target Detection and Tracking in a Heterogeneous Environment with Multiple Resource-Constrained Sensors

Anthony Thompson, Derek Paley

Inspired by the periodic swimming of many fish species, this paper presents a dynamic model of self-propelled particles with a periodic controller.

CDCL paper submitted to ACC 2022

2021

Multi-Target Detection and Tracking in a Heterogeneous Environment with Multiple Resource-Constrained Sensors

Curtis Merrill, Derek Paley

The authors consider the multiple-sensor multiple-target detection and tracking problem in a sensor network consisting of resource-constrained multi-function radars.

AIAA SciTech Forum and Exhibition 2022

Bilinearization, Reachability, and Optimal Control of Control-Affine Nonlinear Systems: A Koopman Spectral Approach

Debdipta Goswami, Derek Paley

This paper considers the problem of bilinearization and optimal control of a control-affine nonlinear system by projecting the system dynamics onto the Koopman eigenspace. Although there are linearization techniques like Carleman linearization for embedding a finite-dimensional nonlinear system into an infinite-dimensional space, they depend on the analytic property of the vector fields and work only on polynomial space. The proposed method utilizes the Koopman Canonical Transform, specifically the Koopman eigenfunctions of the drift vector field, to transform the dynamics into a bilinear system under certain assumptions.

IEEE Transactions on Automatic Control

Optimal control of a 2D diffusion-advection process with a team of mobile actuators under jointly optimal guidance

Sheng Cheng, Derek Paley

This paper describes an optimization framework to control a distributed parameter system using a team of mobile actuators.

Paper submitted to Automatica

Optimal guidance of a team of mobile actuators for controlling a 1D diffusion process with unknown initial conditions

Sheng Cheng, Derek Paley

Proposes an optimization frameworkfor steering a team of mobile actuators to control a diffusionprocess with unknown initial conditions.

CDCL paper

Data-driven estimation using an Echo-State Neural Network equipped with an Ensemble Kalman Filter

Debdipta Goswami, Artur Wolek, Derek Paley

Considers the problem of data-driven estimation with sparse measurements for a complex nonlinear system. While model-based nonlinear estimation methods are well known, state estimation from partial observations with unmodeled dynamics is less understood. The authors use a method for model-free estimation based on an echo-state network (ESN)where a reasonably accurate set of training data is available during the training period and some sparse measurements are obtained during the testing phase.

CDCL paper

Feedback Control and Parameter Estimation for Lift Maximization of a Pitching Airfoil

Justin Lidard, Debdipta Goswami, David Snyder, Girguis Sedky, Anya Jones, Derek Paley

Proposes state- and output-feedback controls for a pitching airfoil in an unsteady flow using the Goman–Khabrov model.

Journal of Guidance, Control and Dynamics

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

2020

Autonomous Flight-Test Data in Support of Safety of Flight Certification

Donald H. Costello III, Jason Jewell, Huan Xu

The current safety of flight clearances for unmanned aircraft requires a qualified operator who can make decisions and ultimately bears the responsibility for the safe operations of the vehicle. The future of aviation is unmanned, and ultimately autonomous. Yet, a method for certifying an autonomous vehicle to make decisions currently reserved for qualified pilots does not exist. Before we can field autonomous systems, a process needs to be approved to certify them.

This paper analyzes the flight-test data (both developmental and operational) of an autonomous decision engine selecting an appropriate landing site for a large rotorcraft in an unprepared landing zone. In particular, this paper focuses on using legacy test and evaluation methods to determine their suitability for obtaining a safety of flight clearance for a system that possesses autonomous functionality.

The autonomous system under test was able to complete a mission currently reserved for qualified pilots under controlled conditions. However, when confronted with conditions that were not anticipated (or programmed), the software lacked the judgment a pilot uses to complete a mission under off-nominal conditions.

AIAA Journal of Air Transportation

Mitigation of Ground Impact Hazard for Safe Unmanned Aerial Vehicle Operations

Andrew Poissant, Lina Castano, Huan Xu

The researchers have developed a UAV hazard mitigation software that guards against ground impact in highly populated areas. Successful incorporation of the ground impact and hazard mitigation (GIHM) module into UAV software reduces fatalities per flight hour and brings UAVs closer to being safe enough for integration into the NAS.

AIAA Journal of Aerospace Information Systems


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