pwc-archive/papers-with-abstracts huggingface dataset:
Security and Privacy of Digital Twins for Advanced Manufacturing: A Survey
Authors: Alexander D. Zemskov, Yao Fu, Runchao Li, Xufei Wang, Vispi Karkaria, Ying-Kuan Tsai, Wei Chen, Jianjing Zhang, Robert Gao, Jian Cao, Kenneth A. Loparo, Pan Li
Date: 2024-12-18 00:00:00

In Industry 4.0, the digital twin is one of the emerging technologies, offering simulation abilities to predict, refine, and interpret conditions and operations, where it is crucial to emphasize a heightened concentration on the associated security and privacy risks. To be more specific, the adoption of digital twins in the manufacturing industry relies on integrating technologies like cyber-physical systems, the Industrial Internet of Things, virtualization, and advanced manufacturing. The interactions of these technologies give rise to numerous security and privacy vulnerabilities that remain inadequately explored. Towards that end, this paper analyzes the cybersecurity threats of digital twins for advanced manufacturing in the context of data collection, data sharing, machine learning and deep learning, and system-level security and privacy. We also provide several solutions to the threats in those four categories that can help establish more trust in digital twins.
Achieving Dispatchability in Data Centers: Carbon and Cost-Aware Sizing of Energy Storage and Local Photovoltaic Generation
Authors: Enea Figini, Mario Paolone
Date: 2024-12-18 00:00:00

Data centers are large electricity consumers due to the high consumption needs of servers and their cooling systems. Given the current crypto-currency and artificial intelligence trends, the data center electricity demand is bound to grow significantly. With the electricity sector being responsible for a large share of global greenhouse gas (GHG) emissions, it is important to lower the carbon footprint of data centers to meet GHG emissions targets set by international agreements. Moreover, uncontrolled integration of data centers in power distribution grids contributes to increasing the stochasticity of the power system demand, thus increasing the need for capacity reserves, which leads to economic and environmental inefficiencies in the power grid operation. This work provides a method to size a PhotoVoltaic (PV) system and an Energy Storage System (ESS) for an existing data center looking to reduce both its carbon footprint and demand stochasticity via dispatching. The proposed scenario-based optimization framework allows to size the ESS and the PV system to minimize the expected operational and capital costs, along with the carbon footprint of the data center complex. The life cycle assessment of the resources, as well as the dynamic carbon emissions of the upstream power distribution grid, are accounted for while computing the day-ahead planning of the data center aggregated demand and PV generation. Case studies in different Swiss cantons and regions of Germany emphasize the need for location-aware sizing processes since the obtained optimal solutions strongly depend on the local electricity carbon footprint, cost and on the local irradiance conditions. Some regions show potential in carbon footprint reduction, while other regions do not.
Fast Link Recovery via PTP-synchronized Nanosecond Optical Switching
Authors: V. Yokar, A. Mehrpooya, Y. Teng, S. Shen, Z. Wu, K. Bardhi, S. Yan, D. Simeonidou
Date: 2024-12-18 00:00:00

This paper proposes and validates a PTP-synchronized 8.4ns optical switching with a 100ns jitter at the switching edges. This approach is adopted and demonstrated for instant network recovery within 2.7ms and scheduled network recovery.
Aggregative games with bilevel structures: Distributed algorithms and convergence analysis
Authors: Kaihong Lu, Huanshui Zhang, Long Wang
Date: 2024-12-18 00:00:00

In this paper, the problem of distributively seeking the equilibria of aggregative games with bilevel structures is studied. Different from the traditional aggregative games, here the aggregation is determined by the minimizer of a virtual leader's objective function in the inner level, which depends on the actions of the players in the outer level. Moreover, the global objective function of the virtual leader is formed by the sum of some local functions with two arguments, each of which is strongly convex with respect to the second argument. When making decisions, each player in the outer level only has access to a local part of the virtual leader's objective function. To handle this problem, first, we propose a second order gradient-based distributed algorithm, where the Hessian matrices associated with the objective functions of the leader are involved. By the algorithm, players update their actions while cooperatively minimizing the objective function of the virtual leader to estimate the aggregation by communicating with their neighbors via a connected graph. Under mild assumptions on the graph and cost functions, we prove that the actions of players asymptotically converge to the Nash equilibrium point. Then, for the case where the Hessian matrices associated with the objective functions of the virtual leader are not available, we propose a first order gradient-based distributed algorithm, where a distributed two-point estimate strategy is developed to estimate the gradients of players' cost functions in the outer level. Under the same conditions, we prove that the convergence errors of players' actions to the Nash equilibrium point are linear with respect to the estimate parameters. Finally, simulations are provided to demonstrate the effectiveness of our theoretical results.
PR-CARA: Proactive V2X Resource Allocation with Extended 1-Stage SCI and Deep Learning-based Sensing Matrix Estimator
Authors: Taesik Nam, Seungjae Lee, Kiwoong Park, Sunbeom Kwon, Nathan Jeong, Han-Shin Jo, Jong-Gwan Yook
Date: 2024-12-18 00:00:00
Tasks: Collision Avoidance

Distributed resource allocation algorithms differ from centralized methods by relying on locally collected information for resource selection, leading to a low vehicle-to-everything (V2X) communication quality of service (QoS) in high-traffic congestion. To overcome these challenges, this study proposes a proactive received signal strength indicator (RSSI)-based collision avoidance resource allocation (PR-CARA) algorithm. This algorithm features an extended 1-stage SCI system, which is a critical component that enables resource monitoring of adjacent vehicle user equipment (VUE). Monitored resources were then processed through a deep learning-based proactive RSSI estimator. The estimated proactive RSSI helps avoid resource selection, which leads to packet collisions, thereby significantly reducing the occurrence of this issue during resource allocation. The proposed algorithm is tested in a cooperative adaptive cruise control (CACC)-based platoon driving scenario that requires ultra-reliable and low-latency communication (URLLC) performance. Simulation results demonstrate that the proposed deep-learning-based proactive resource allocation algorithm, with the extended 1-stage SCI system, reduces packet collisions and improves the transmission signal-to-interference-plus-noise ratio (SINR), thereby significantly enhancing communication reliability compared to the benchmark resource allocation algorithm.
The networked input-output economic problem
Authors: Minh Hoang Trinh, Nhat-Minh Le-Phan, Hyo-Sung Ahn
Date: 2024-12-18 00:00:00

In this paper, we formulate an input-output economic model with multiple interactive economic systems. The model captures the multi-dimensional nature of the economic sectors or industries in each economic system, the interdependencies among industries within an economic system and across different economic systems, and the influence of demand. To determine the equilibrium price structure of the model, a matrix-weighted updating algorithm is proposed. We prove that the equilibrium price structure can be globally asymptotically achieved given that certain joint conditions on the matrix-weighted graph and the input-output matrices are satisfied. The theoretical results are then supported by numerical simulations.
Phase Robustness Analysis for Structured Perturbations in MIMO LTI Systems
Authors: Luke Woolcock, Robert Schmid
Date: 2024-12-18 00:00:00

The stability of interconnected linear time-invariant systems using singular values and the small gain theorem has been studied for many decades. The methods of mu-analysis and synthesis has been extensively developed to provide robustness guarantees for a plant subject to structured perturbations, with components in the structured perturbation satisfying a bound on their largest singular value. Recent results on phase-based stability measures have led to a counterpart of the small gain theorem, known as the small phase theorem. To date these phase-based methods have only been used to provide stability robustness measures for unstructured perturbations. In this paper, we define a phase robustness metric for multivariable linear time-invariant systems in the presence of a structured perturbation. We demonstrate its relationship to a certain class of multiplier functions for integral quadratic constraints, and show that a upper bound can be calculated via a linear matrix inequality problem. When combined with robustness measures from the small gain theorem, the new methods are able provide less conservative robustness metrics than can be obtained via conventional mu-analysis methods.
Koopman Mode-Based Detection of Internal Short Circuits in Lithium-ion Battery Pack
Authors: Sanchita Ghosh, Soumyoraj Mallick, Tanushree Roy
Date: 2024-12-17 00:00:00

Monitoring of internal short circuit (ISC) in Lithium-ion battery packs is imperative to safe operations, optimal performance, and extension of pack life. Since ISC in one of the modules inside a battery pack can eventually lead to thermal runaway, it is crucial to detect its early onset. However, the inaccuracy and aging variability of battery models and the unavailability of adequate ISC datasets pose several challenges for both model-based and data-driven approaches. Thus, in this paper, we proposed a model-free Koopman Mode-based module-level ISC detection algorithm for battery packs. The algorithm adopts two parallel Koopman mode generation schemes with the Arnoldi algorithm to capture the Kullback-Leibler divergence-based distributional deviations in Koopman mode statistics in the presence of ISC. Our proposed algorithm utilizes module-level voltage measurements to accurately identify the shorted battery module of the pack without using specific battery models or pre-training with historical battery data. Furthermore, we presented two case studies on shorted battery module detection under both resting and charging conditions. The simulation results illustrated the sensitivity of the proposed algorithm toward ISC and the robustness against measurement noise.
Higher-Order Sinusoidal Input Describing Functions for Open-Loop and Closed-Loop Reset Control with Application to Mechatronics Systems
Authors: Xinxin Zhang, S. Hassan HosseinNia
Date: 2024-12-17 00:00:00

Reset control enhances the performance of high-precision mechatronics systems. This paper introduces a generalized reset feedback control structure that integrates a single reset-state reset controller, a shaping filter for tuning reset actions, and linear compensators arranged in series and parallel configurations with the reset controller. This structure offers greater tuning flexibility to optimize reset control performance. However, frequency-domain analysis for such systems remains underdeveloped. To address this gap, this study makes three key contributions: (1) developing Higher-Order Sinusoidal Input Describing Functions (HOSIDFs) for open-loop reset control systems; (2) deriving HOSIDFs for closed-loop reset control systems and establishing a connection with open-loop analysis; and (3) creating a MATLAB-based App to implement these methods, providing mechatronics engineers with a practical tool for reset control system design and analysis. The accuracy of the proposed methods is validated through simulations and experiments. Finally, the utility of the proposed methods is demonstrated through case studies that analyze and compare the performance of three controllers: a PID controller, a reset controller, and a shaped reset controller on a precision motion stage. Both analytical and experimental results demonstrate that the shaped reset controller provides higher tracking precision while reducing actuation forces, outperforming both the reset and PID controllers. These findings highlight the effectiveness of the proposed frequency-domain methods in analyzing and optimizing the performance of reset-controlled mechatronics systems.
Adaptive Economic Model Predictive Control: Performance Guarantees for Nonlinear Systems
Authors: Maximilian Degner, Raffaele Soloperto, Melanie N. Zeilinger, John Lygeros, Johannes Köhler
Date: 2024-12-17 00:00:00
Tasks: Model Predictive Control

We consider the problem of optimizing the economic performance of nonlinear constrained systems subject to uncertain time-varying parameters and bounded disturbances. In particular, we propose an adaptive economic model predictive control (MPC) framework that: (i) directly minimizes transient economic costs, (ii) addresses parametric uncertainty through online model adaptation, (iii) determines optimal setpoints online, and (iv) ensures robustness by using a tube-based approach. The proposed design ensures recursive feasibility, robust constraint satisfaction, and a transient performance bound. In case the disturbances have a finite energy and the parameter variations have a finite path length, the asymptotic average performance is (approximately) not worse than the performance obtained when operating at the best reachable steady-state. We highlight performance benefits in a numerical example involving a chemical reactor with unknown time-invariant and time-varying parameters.
Prediction-Based Control Barrier Functions for Input-Constrained Safety Critical Systems
Authors: Ali Mesbah, Seid H. Pourtakdoust, Alireza Sharifi, Afshin Banazadeh
Date: 2024-12-17 00:00:00
Tasks: Prediction

Control barrier functions (CBFs) have emerged as a popular topic in safety critical control due to their ability to provide formal safety guarantees for dynamical systems. Despite their powerful capabilities, the determination of feasible CBFs for input-constrained systems is still a formidable task and a challenging research issue. The present work aims to tackle this problem by focusing on an alternative approach towards a generalization of some ideas introduced in the existing CBF literature. The approach provides a rigorous yet straightforward method to define and implement prediction-based control barrier functions for complex dynamical systems to ensure safety with bounded inputs. This is accomplished by introducing a prediction-based term into the CBF that allows for the required margin needed to null the CBF rate of change given the specified input constraints. Having established the theoretical groundwork, certain remarks are subsequently presented with regards to the scheme's implementation. Finally, the proposed prediction-based control barrier function (PB-CBF) scheme is implemented for two numerical examples. In particular, the second example is related to aircraft stall prevention, which is meant to demonstrate the functionality and capability of the PB-CBFs in handling complex nonlinear dynamical systems via simulations. In both examples, the performance of the PB-CBF is compared with that of a non-prediction based basic CBF.
Comparative Analysis of Zero-Shot Capability of Time-Series Foundation Models in Short-Term Load Prediction
Authors: Nan Lin, Dong Yun, Weijie Xia, Peter Palensky, Pedro P. Vergara
Date: 2024-12-17 00:00:00
Tasks: Time Series

Short-term load prediction (STLP) is critical for modern power distribution system operations, particularly as demand and generation uncertainties grow with the integration of low-carbon technologies, such as electric vehicles and photovoltaics. In this study, we evaluate the zero-shot prediction capabilities of five Time-Series Foundation Models (TSFMs)-a new approach for STLP where models perform predictions without task-specific training-against two classical models, Gaussian Process (GP) and Support Vector Regression (SVR), which are trained on task-specific datasets. Our findings indicate that even without training, TSFMs like Chronos, TimesFM, and TimeGPT can surpass the performance of GP and SVR. This finding highlights the potential of TSFMs in STLP.
Safe Trajectory Sets for Online Operation of Power Systems under Uncertainty
Authors: Florian Klein-Helmkamp, Tina Möllemann, Irina Zettl, Steffen Kortmann, Andreas Ulbig
Date: 2024-12-17 00:00:00

Flexibility provision from active distribution grids requires efficient and robust methods of optimization and control suitable to online operation. In this paper we introduce conditions for the safe operation of feedback optimization based controllers. We use the feasible operating region of a controlled system as bounds for safe system states and evaluate the trajectories of the controller based on the projection of the full system state onto the two-dimensional PQ-plane. We demonstrate the defined conditions for an exemplary sub-transmission system. We show that the proposed method is suitable to evaluate controller performance and robustness for systems subject to disturbances.
A MARL Based Multi-Target Tracking Algorithm Under Jamming Against Radar
Authors: Ziang Wang, Lei Wang, Qi Yi, Yimin Liu
Date: 2024-12-17 00:00:00
Tasks: Multi-agent Reinforcement Learning

Unmanned aerial vehicles (UAVs) have played an increasingly important role in military operations and social life. Among all application scenarios, multi-target tracking tasks accomplished by UAV swarms have received extensive attention. However, when UAVs use radar to track targets, the tracking performance can be severely compromised by jammers. To track targets in the presence of jammers, UAVs can use passive radar to position the jammer. This paper proposes a system where a UAV swarm selects the radar's active or passive work mode to track multiple differently located and potentially jammer-carrying targets. After presenting the optimization problem and proving its solving difficulty, we use a multi-agent reinforcement learning algorithm to solve this control problem. We also propose a mechanism based on simulated annealing algorithm to avoid cases where UAV actions violate constraints. Simulation experiments demonstrate the effectiveness of the proposed algorithm.
Controllability and Tracking of Ensembles: An Optimal Transport Theory Viewpoint
Authors: Reza Hadadi
Date: 2024-12-17 00:00:00

This paper explores the controllability and state tracking of ensembles from the perspective of optimal transport theory. Ensembles, characterized as collections of systems evolving under the same dynamics but with varying initial conditions, are a fundamental concept in control theory and applications. By leveraging optimal transport, we provide a novel framework for analyzing and solving the state tracking problem of ensembles, particularly when state observations are limited and only accessible at discrete time points. This study establishes connections between the ensemble dynamics and finite-horizon optimal control problems, demonstrating that the problem can be reformulated as a computationally efficient linear program using Kantorovich's formulation of optimal transport. We raise notions of observability and controllability for nonlinear ensembles, and propose methods for state tracking in Gaussian output distributions settings. Numerical examples and theoretical insights are provided to validate the approach, highlighting the utility of optimal transport in ensemble control problems.
Multi-UAV Collaborative Trajectory Planning for Seamless Data Collection and Transmission
Authors: Rui Wang, Kaitao Meng, Deshi Li
Date: 2024-12-17 00:00:00
Tasks: Scheduling, Trajectory Planning

Unmanned aerial vehicles (UAVs) have attracted plenty of attention due to their high flexibility and enhanced communication ability. However, the limited coverage and energy of UAVs make it difficult to provide timely wireless service for large-scale sensor networks, which also exist in multiple UAVs. To this end, the advanced collaboration mechanism of UAVs urgently needs to be designed. In this paper, we propose a multi-UAV collaborative scheme for seamless data collection and transmission, where UAVs are dispatched to collection points (CPs) to collect and transmit the time-critical data to the ground base station (BS) simultaneously through the cooperative backhaul link. Specifically, the mission completion time is minimized by optimizing the trajectories, task allocation, collection time scheduling, and transmission topology of UAVs while ensuring backhaul link to the BS. However, the formulated problem is non-convex and challenging to solve directly. To tackle this problem, the CP locations and transmission topology of UAVs are obtained by sensor node (SN) clustering and region division. Next, the transmission connectivity condition between UAVs is derived to facilitate the trajectory discretization and thus reduce the dimensions of variables. This simplifies the problem to optimizing the UAV hovering locations, hovering time, and CP serving sequence. Then, we propose a point-matching-based trajectory planning algorithm to solve the problem efficiently. The simulation results show that the proposed scheme achieves significant performance gains over the two benchmarks.
Circuit-Theoretic Joint Parameter-State Estimation of Utility-Scale Photovoltaic, Battery, and Grid Systems
Authors: Peng Sang, Amritanshu Pandey
Date: 2024-12-17 00:00:00
Tasks: State Estimation

Solar PV and battery storage systems have become integral to modern power grids. Therefore, bulk grid models in real-time operation must include their physical behavior accurately for analysis and optimization. AC state estimation is critical to building real-time bulk power systems models. However, current ACSE techniques do not include detailed physics and measurements for battery and PV systems. This results in sub-optimal estimation results and subsequent less accurate bulk grid models for real-time operation. To address these challenges, we formulate a circuit-theoretic AC state estimator with accurate PV and battery systems physics and corresponding measurements. First, we propose an aggregated equivalent circuit model of the solar PV, battery, and traditional grid components. Next, we add measurements from PV and battery systems to the traditional measurement set to facilitate accurate estimation of the overall grid model. Finally, we develop a circuit-theoretic joint parameter-state estimation algorithm that can accurately estimate grid, PV, and battery system states and is robust against erroneous parameters. To demonstrate the efficacy of the proposed framework, we estimate the states of 10k node transmission networks with hundreds of battery+PV-tied systems. We compare the accuracy against the estimation of stand-alone grid, battery, and PV systems.