2021
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
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
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
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
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)
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)
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)
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)
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
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)
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
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
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
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
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
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
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
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
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)
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
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
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
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
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
Usman Fiaz, John Baras
A hybrid compositional approach to time-critical search and rescue planning for quadrotor UAVs.
arXiv.org
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
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
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
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
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
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
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
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
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)
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
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
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
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
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
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
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
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
+++++++++
2020
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
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
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
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
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
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
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
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
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
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
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
2019
Senthil Hariharan Arul, Dinesh Manocha
A new algorithm for decentralized collision avoidance for quadrotor swarm navigation in dense environments with static and dynamic obstacles.
arXiv.org
2020
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
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
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
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
2021
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
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
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
Sheng Cheng, Derek Paley
Proposes an optimization frameworkfor steering a team of mobile actuators to control a diffusionprocess with unknown initial conditions.
CDCL paper
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
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
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
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
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
2021
M. Paul Laiu, André L. Tits
An exact-penalty-based framework for allowing for infeasible starts in solving CQPs (including linear optimization problems. With negligible additional computational cost per iteration, an infeasibility test is included that provides an infeasibility certificate when the problem at hand is indeed infeasible.
Mathematical Programming
2020
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
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
