pwc-archive/papers-with-abstracts huggingface dataset:
A Design Method of Distributed Algorithms via Discrete-time Blended Dynamics Theorem
Authors: Jeong Woo Kim, Jin Gyu Lee, Donggil Lee, Hyungbo Shim
Date: 2022-10-11 00:00:00

We develop a discrete-time version of the blended dynamics theorem for the use of designing distributed computation algorithms. The blended dynamics theorem enables to predict the behavior of heterogeneous multi-agent systems. Therefore, once we get a blended dynamics for a particular computational task, design idea of node dynamics for individual heterogeneous agents can easily occur. In the continuous-time case, prediction by blended dynamics was enabled by high coupling gain among neighboring agents. In the discrete-time case, we propose an equivalent action, which we call multi-step coupling in this paper. Compared to the continuous-time case, the blended dynamics can have more variety depending on the coupling matrix. This benefit is demonstrated with three applications; distributed estimation of network size, distributed computation of the PageRank, and distributed computation of the degree sequence of a graph, which correspond to the coupling by doubly-stochastic, column-stochastic, and row-stochastic matrices, respectively.
Convergence of Bi-Virus Epidemic Models with Non-Linear Rates on Networks -- A Monotone Dynamical Systems Approach
Authors: Vishwaraj Doshi, Shailaja Mallick, Do Young Eun
Date: 2022-10-11 00:00:00

We study convergence properties of competing epidemic models of the Susceptible-Infected-Susceptible (SIS) type. The SIS epidemic model has seen widespread popularity in modelling the spreading dynamics of contagions such as viruses, infectious diseases, or even rumors/opinions over contact networks (graphs).We analyze the case of two such viruses spreading on overlaid graphs, with non-linear rates of infection spread and recovery. We call this the non-linear bi-virus model and, building upon recent results, obtain precise conditions for global convergence of the solutions to a trichotomy of possible outcomes: a virus-free state, a single-virus state, and to a coexistence state. Our techniques are based on the theory of monotone dynamical systems (MDS), in contrast to Lyapunov based techniques that have only seen partial success in determining convergence properties in the setting of competing epidemics. We demonstrate how the existing works have been unsuccessful in characterizing a large subset of the model parameter space for bi-virus epidemics, including all scenarios leading to coexistence of the epidemics. To the best of our knowledge, our results are the first in providing complete convergence analysis for the bi-virus system with nonlinear infection and recovery rates on general graphs.
Optimal Hybrid Multiplexed AC-DC-AC converters
Authors: Matthew Deakin
Date: 2022-10-10 00:00:00

The flexibility of multi-terminal AC-DC-AC converters connected in distribution networks can be increased by changing the sizes of the individual AC-DC converter stages and connecting the AC side of those converters to electromechanical switches (multiplexers) to allow reconfiguration within the network. The combinations of real powers that can be transferred by such a design can be described using a capability chart. In this work, it is proposed that the area of these capability charts is a meaningful metric for describing the flexibility of such a device. These capability chart areas are calculated in closed form for a three-terminal AC-DC-AC device consisting of three AC-DC converters of arbitrary sizes, allowing the optimal AC-DC converter sizing to be determined to maximise this area. It is shown that this optimal design yields a capability chart area that is 64% larger than the equivalent area from a conventional equally-sized AC-DC-AC converter. Converters which are optimal in other senses are discussed, such as a design with 10% increased per-feeder real power transfer, albeit with an 8% area reduction. It is concluded that the capability chart area is an intuitive and informative approach for describing the increased flexibility of multiplexed AC-DC-AC converters.
Energy-efficient Reactive and Predictive Connected Cruise Control
Authors: Minghao Shen, R. Austin Dollar, Tamas G. Molnar, Chaozhe R. He, Ardalan Vahidi, Gabor Orosz
Date: 2022-10-10 00:00:00

In this paper, we propose a framework for the longitudinal control of connected and automated vehicles traveling in mixed traffic consisting of connected and non-connected human-driven vehicles. Reactive and predictive controllers are proposed. Reactive controllers are given by explicit feedback control laws. In predictive controllers, the control input is optimized in a receding-horizon fashion, which depends on the predictions of motions of preceding vehicles. Beyond-line-of-sight information is obtained via vehicle-to-vehicle (V2V) communication, and is utilized in the proposed reactive and predictive controllers. Simulations utilizing real traffic data are used to show that connectivity can bring significant energy savings.
Propagation Stability Concepts for Network Synchronization Processes
Authors: Sandip Roy, Subir Sarker, Mengran Xue
Date: 2022-10-09 00:00:00
Tasks: Unity

A notion of disturbance propagation stability is defined for dynamical network processes, in terms of decrescence of an input-output energy metric along cutsets away from the disturbance source. A characterization of the disturbance propagation notion is developed for a canonical model for synchronization of linearly-coupled homogeneous subsystems. Specifically, propagation stability is equivalenced with the frequency response of a certain local closed-loop model, which is defined from the subsystem model and local network connections, being sub-unity gain. For the case where the subsystem is single-input single-output (SISO), a further simplification in terms of the subsystem's open loop Nyquist plot is obtained. An extension of the disturbance propagation stability concept toward imperviousness of subnetworks to disturbances is briefly developed, and an example focused on networks with planar subsystems is considered.
California Test System (CATS): A Geographically Accurate Test System based on the California Grid
Authors: Sofia Taylor, Aditya Rangarajan, Noah Rhodes, Jonathan Snodgrass, Bernie Lesieutre, Line A. Roald
Date: 2022-10-09 00:00:00

This paper presents the California Test System (CATS), a synthetic transmission grid in California that can be used by the public for power systems policy research without revealing any critical energy information. The proposed synthetic grid combines publicly available geographic data of California's electric infrastructure, such as the actual locations of transmission corridors, with invented topology and transmission line parameters that are ``realistic but not real". The result is a power grid test system that is suitable for power flow and policy analyses with geo-referenced applications, including studies related to weather, topography, and socio-economic considerations. The methods used to develop and evaluate the CATS grid are documented in detail in this report.
Equivalence of Optimality Criteria for Markov Decision Process and Model Predictive Control
Authors: Arash Bahari Kordabad, Mario Zanon, Sebastien Gros
Date: 2022-10-09 00:00:00
Tasks: Model Predictive Control, reinforcement-learning, Reinforcement Learning (RL)

This paper shows that the optimal policy and value functions of a Markov Decision Process (MDP), either discounted or not, can be captured by a finite-horizon undiscounted Optimal Control Problem (OCP), even if based on an inexact model. This can be achieved by selecting a proper stage cost and terminal cost for the OCP. A very useful particular case of OCP is a Model Predictive Control (MPC) scheme where a deterministic (possibly nonlinear) model is used to reduce the computational complexity. This observation leads us to parameterize an MPC scheme fully, including the cost function. In practice, Reinforcement Learning algorithms can then be used to tune the parameterized MPC scheme. We verify the developed theorems analytically in an LQR case and we investigate some other nonlinear examples in simulations.
Optimal Control for Platooning in Vehicular Networks
Authors: Thiago S. Gomides, Evangelos Kranakis, Ioannis Lambadaris, Yannis Viniotis
Date: 2022-10-09 00:00:00
Tasks: Autonomous Driving

As the automotive industry is developing autonomous driving systems and vehicular networks, attention to truck platooning has increased as a way to reduce costs (fuel consumption) and improve efficiency in the highway. Recent research in this area has focused mainly on the aerodynamics, network stability, and longitudinal control of platoons. However, the system aspects (e.g., platoon coordination) are still not well explored. In this paper, we formulate a platooning coordination problem and study whether trucks waiting at an initial location (station) should wait for a platoon to arrive in order to leave. Arrivals of trucks at the station and platoons by the station are modelled by independent Bernoulli distributions. Next we use the theory of Markov Decision Processes to formulate the dispatching control problem and derive the optimal policy governing the dispatching of trucks with platoons. We show that the policy that minimizes an average cost function at the station is of threshold type. Numerical results for the average cost case are presented. They are consistent with the optimal ones.
Robust Adaptive Neural Network Control of Time-Varying State Constrained Nonlinear Systems
Authors: Pankaj Kumar Mishra, Nishchal K Verma
Date: 2022-10-09 00:00:00

This paper deals with the tracking control problem for a very simple class of unknown nonlinear systems. In this paper, we presents a design strategy for tracking control of time-varying state constrained nonlinear systems in an adaptive framework. The controller is designed using the backstepping method. While designing it, Barrier Lyapunov Function (BLF) is used so that the state variables do not contravene its constraints. In order to cope with the unknown dynamics of the system, an online approximator is designed using a neural network with a novel adaptive law for its weight update. To make the controller robust and computationally inexpensive, a disturbance observer is proposed to cope with the disturbance along with neural network approximation error and the time derivative of virtual control input. The effectiveness of the proposed approach is demonstrated through a simulation study.
Adaptive Control of Unknown Pure Feedback Systems with Pure State Constraints
Authors: Pankaj Kumar Mishra, Nishchal K Verma
Date: 2022-10-09 00:00:00

This paper deals with the tracking control problem for a class of unknown pure feedback system with pure state constraints on the state variables and unknown time-varying bounded disturbances. An adaptive controller is presented for such systems for the very first time. The controller is designed using the backstepping method. While designing it, Barrier Lyapunov Functions is used so that the state variables do not contravene its constraints. In order to cope with the unknown dynamics of the system, an online approximator is designed using a neural network with a novel adaptive law for its weight update. In the stability analysis of the system, the time derivative of Lyapunov function involves known virtual control coefficient with unknown direction and to deal with such problem Nussbaum gain is used to design the control law. Furthermore, to make the controller robust and computationally inexpensive, a novel disturbance observer is designed to estimate the disturbance along with neural network approximation error and the time derivative of virtual control input. The effectiveness of the proposed approach is demonstrated through a simulation study on the third-order nonlinear system.
A State Feedback Controller for Mitigation of Continuous-Time Networked SIS Epidemics
Authors: YuAn Wang, Sebin Gracy, César A. Uribe, Hideaki Ishii, Karl Henrik Johansson
Date: 2022-10-09 00:00:00

The paper considers continuous-time networked susceptible-infected-susceptible (SIS) diseases spreading over a population. Each agent represents a sub-population and has its own healing rate and infection rate; the state of the agent at a time instant denotes what fraction of the said sub-population is infected with the disease at the said time instant. By taking account of the changes in behaviors of the agents in response to the infection rates in real-time, our goal is to devise a feedback strategy such that the infection level for each agent strictly stays below a pre-specified value. Furthermore, we are also interested in ensuring that the closed-loop system converges either to the disease-free equilibrium or, when it exists, to the endemic equilibrium. The upshot of devising such a strategy is that it allows health administration officials to ensure that there is sufficient capacity in the healthcare system to treat the most severe cases. We demonstrate the effectiveness of our controller via numerical examples.
A Contextual Bandit Approach for Value-oriented Prediction Interval Forecasting
Authors: Yufan Zhang, Honglin Wen, Qiuwei Wu
Date: 2022-10-09 00:00:00

Prediction interval (PI) is an effective tool to quantify uncertainty and usually serves as an input to downstream robust optimization. Traditional approaches focus on improving the quality of PI in the view of statistical scores and assume the improvement in quality will lead to a higher value in the power systems operation. However, such an assumption cannot always hold in practice. In this paper, we propose a value-oriented PI forecasting approach, which aims at reducing operational costs in downstream operations. For that, it is required to issue PIs with the guidance of operational costs in robust optimization, which is addressed within the contextual bandit framework here. Concretely, the agent is used to select the optimal quantile proportion, while the environment reveals the costs in operations as rewards to the agent. As such, the agent can learn the policy of quantile proportion selection for minimizing the operational cost. The numerical study regarding a two-timescale operation of a virtual power plant verifies the superiority of the proposed approach in terms of operational value. And it is especially evident in the context of extensive penetration of wind power.
Controlling a Markov Decision Process with an Abrupt Change in the Transition Kernel
Authors: Nathan Dahlin, Subhonmesh Bose, Venugopal V. Veeravalli
Date: 2022-10-08 00:00:00
Tasks: Change Detection

We consider the control of a Markov decision process (MDP) that undergoes an abrupt change in its transition kernel (mode). We formulate the problem of minimizing regret under control-switching based on mode change detection, compared to a mode-observing controller, as an optimal stopping problem. Using a sequence of approximations, we reduce it to a quickest change detection (QCD) problem with Markovian data, for which we characterize a state-dependent threshold-type optimal change detection policy. Numerical experiments illustrate various properties of our control-switching policy.
A Zero-Sum Game Framework for Optimal Sensor Placement in Uncertain Networked Control Systems under Cyber-Attacks
Authors: Anh Tung Nguyen, Sribalaji C. Anand, André M. H. Teixeira
Date: 2022-10-08 00:00:00

This paper proposes a game-theoretic approach to address the problem of optimal sensor placement against an adversary in uncertain networked control systems. The problem is formulated as a zero-sum game with two players, namely a malicious adversary and a detector. Given a protected performance vertex, we consider a detector, with uncertain system knowledge, that selects another vertex on which to place a sensor and monitors its output with the aim of detecting the presence of the adversary. On the other hand, the adversary, also with uncertain system knowledge, chooses a single vertex and conducts a cyber-attack on its input. The purpose of the adversary is to drive the attack vertex as to maximally disrupt the protected performance vertex while remaining undetected by the detector. As our first contribution, the game payoff of the above-defined zero-sum game is formulated in terms of the Value-at-Risk of the adversary's impact. However, this game payoff corresponds to an intractable optimization problem. To tackle the problem, we adopt the scenario approach to approximately compute the game payoff. Then, the optimal monitor selection is determined by analyzing the equilibrium of the zero-sum game. The proposed approach is illustrated via a numerical example of a 10-vertex networked control system.
Towards Joint Electricity and Data Trading: A Scalable Cooperative Game Theoretic Approach
Authors: Mingyu Yan, Fei Teng
Date: 2022-10-08 00:00:00
Tasks: Imputation

This paper, for the first time, proposes a joint electricity and data trading mechanism based on cooperative game theory. All prosumers first submit the parameters associated with both electricity and data to the market operator. The operator utilizes the public and prosumers' private data to forecast the distributed renewable generators (DRGs) and quantify the improvement driven by prosumers' private data in terms of reduced uncertainty set. Then, the operator maximizes the grand coalition's total payoff considering the uncertain generation of DRGs and imputes the payoff to each prosumer based on their contribution to electricity and data sharing. The mathematical formulation of the grand coalition is developed and converted into a second order cone programming problem by using an affinepolicy based robust approach. The stability of such a grand coalition is mathematically proved, i.e., all prosumers are willing to cooperate. Furthermore, to address the scalability challenge of existing payoff imputation methods in the cooperative game, a two stage optimization based approach is proposed, which is converted into a mixed integer second order cone programming and solved by the Benders decomposition. Case studies illustrate all prosumers are motivated to trade electricity and data under the joint trading framework and the proposed imputation method significantly enhances the scalability.
Wholesale Market Participation of Storage with State-of-Charge Dependent Bids
Authors: Cong Chen, Lang Tong
Date: 2022-10-08 00:00:00

Wholesale market participation of storage with state-of-charge (SoC) dependent bids results in a non-convex cost in a multi-interval economic dispatch, which requires a mixed-integer linear program in the market clearing. We show that the economic dispatch can be convexified to the standard linear program when the SoC-dependent bid satisfies the equal decremental-cost ratio (EDCR) condition. Such EDCR bids are shown to support individual rationalities of all market participants in both the day-ahead multi-interval economic dispatch under locational marginal pricing and the rolling-window look-ahead dispatch under temporal-locational marginal pricing in the real-time market. A numerical example is presented to demonstrate a higher profit margin with an SoC-dependent bid over that from an SoC-independent bid.
Modeling and Analysis of Grid Tied Combined Ultracapacitor Fuel Cell for Renewable Application
Authors: Webster Adepoju, Indranil Bhattacharya, 0lufunke Mary Sanyaolu
Date: 2022-10-06 00:00:00

In this manuscript, the performance of an ultracapacitor fuel cell in grid connected mode is investigated. Voltage regulation to the ultracapacitor was achieved with a three level bidirectional DC-DC converter while also achieving power flow from the grid to the ultra-capacitor via the bidirectional converter. The choice of a bidirectional three level converter for voltage regulation is based on its inherently high efficiency, low harmonic profile and compact size. Using the model equations of the converter and grid connected inverter derived using the switching function approach, the grid's direct and quadrature axes modulation indices, Md and Mq, respectively were simulated in Matlab for both lagging and leading power factors. Moreover, the values of Md and Mq were exploited in a PLECS based simulation of the proposed model to determine the effect of power factor correction on the current and power injection to grid
Equivalent Circuit Modeling and Analysis of Metamaterial Based Wireless Power Transfer
Authors: Webster Adepoju, Indranil Bhattacharya, Ismail Fidan, Nasr Esfahani Ebrahim, 0latunji Abiodun, Ranger Buchanan, Trapa Banik, Muhammad Enagi Bima
Date: 2022-10-06 00:00:00

In this study, an equivalent circuit model is presented to emulate the behavior of a metamaterial-based wireless power transfer system. For this purpose, the electromagnetic field simulation of the proposed system is conducted in ANSYS high frequency structure simulator. In addition, a numerical analysis of the proposed structure is explored to evaluate its transfer characteristics. The power transfer efficiency of the proposed structure is represented by the transmission scattering parameter. While some methods, including interference theory and effective medium theory have been exploited to explain the physics mechanism of MM-based WPT systems, some of the reactive parameters and the basic physical interpretation have not been clearly expounded. In contrast to existing theoretical model, the proposed approach focuses on the effect of the system parameters and transfer coils on the system transfer characteristics and its effectiveness in analyzing complex circuit. Numerical solution of the system transfer characteristics, including the scattering parameter and power transfer efficiency is conducted in Matlab. The calculation results based on numerical estimation validates the full wave electromagnetic simulation results, effectively verifying the accuracy of the analytical model.
Decoding the double trouble: A mathematical modelling of co-infection dynamics of SARS-CoV-2 and influenza-like illness
Authors: Suman Bhowmick, Igor M. Sokolov, Hartmut H. K. Lentz
Date: 2022-10-11 00:00:00

After the detection of coronavirus disease 2019 (Covid-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Wuhan, Hubei Province, China in late December, the cases of Covid-19 have spiralled out around the globe. Due to the clinical similarity of Covid-19 with other flulike syndromes, patients are assayed for other pathogens of influenza like illness. There have been reported cases of co-infection amongst patients with Covid-19. Bacteria for example Streptococcus pneumoniae, Staphylococcus aureus, Klebsiella pneumoniae, Mycoplasma pneumoniae, Chlamydia pneumonia, Legionella pneumophila etc and viruses such as influenza, coronavirus, rhinovirus/enterovirus, parainfluenza, metapneumovirus, influenza B virus etc are identified as co-pathogens. In our current effort, we develop and analysed a compartmental based Ordinary Differential Equation (ODE) type mathematical model to understand the co-infection dynamics of Covid-19 and other influenza type illness. In this work we have incorporated the saturated treatment rate to take account of the impact of limited treatment resources to control the possible Covid-19 cases. As results, we formulate the basic reproduction number of the model system. Finally, we have performed numerical simulations of the co-infection model to examine the solutions in different zones of parameter space.