pwc-archive/papers-with-abstracts huggingface dataset:
Improving Power Systems Controllability via Edge Centrality Measures
Authors: MirSaleh Bahavarnia, Muhammad Nadeem, Ahmad F. Taha
Date: 2025-05-15 00:00:00

Improving the controllability of power networks is crucial as they are highly complex networks operating in synchrony; even minor perturbations can cause desynchronization and instability. To that end, one needs to assess the criticality of key network components (buses and lines) in terms of their impact on system performance. Traditional methods to identify the key nodes/edges in power networks often rely on static centrality measures based on the network's topological structure ignoring the network's dynamic behavior. In this paper, using multi-machine power network models and a new control-theoretic edge centrality matrix (ECM) approach, we: (i) quantify the influence of edges (i.e., the line susceptances) in terms of controllability performance metrics, (ii) identify the most influential lines, and (iii) compute near-optimal edge modifications that improve the power network controllability. Employing various IEEE power network benchmarks, we validate the effectiveness of the ECM-based algorithm and demonstrate improvements in system reachability, control, and damping performance.
Planar Herding of Multiple Evaders with a Single Herder
Authors: Rishabh Kumar Singh, Debraj Chakraborty
Date: 2025-05-15 00:00:00

A planar herding problem is considered, where a superior pursuer herds a flock of non-cooperative, inferior evaders around a predefined target point. An inverse square law of repulsion is assumed between the pursuer and each evader. Two classes of pursuer trajectories are proposed: (i) a constant angular-velocity spiral, and (ii) a constant angular-velocity circle, both centered around the target point. For the spiraling pursuer, the radial velocity is dynamically adjusted based on a feedback law that depends on the instantaneous position of the evader, which is located at the farthest distance from the target at the start of the game. It is shown that, under suitable choices of the model parameters, all the evaders are herded into an arbitrarily small limit cycle around the target point. Meanwhile, the pursuer also converges onto a circular trajectory around the target. The conditions for the stability of these limit cycles are derived. For the circling pursuer, similar guarantees are provided along with explicit formulas for the radii of the limit cycles.
Event-Triggered Synergistic Controllers with Dwell-Time Transmission
Authors: Xuanzhi Zhu, Pedro Casau, Carlos Silvestre
Date: 2025-05-15 00:00:00

We propose novel event-triggered synergistic controllers for nonlinear continuous-time plants by incorporating event-triggered control into stabilizing synergistic controllers. We highlight that a naive application of common event-triggering conditions may not ensure dwell-time transmission due to the joint jumping dynamics of the closed-loop system. Under mild conditions, we develop a suite of event-triggered synergistic controllers that guarantee both dwell-time transmission and global asymptotic stability. Through numerical simulations, we demonstrate the effectiveness of our controller applied to the problem of rigid body attitude stabilization.
Stability and Convergence Analysis of Multi-Agent Consensus with Communication Delays: A Lambert W Function Approach
Authors: Layan Badran, Kiarash Aryankia, Rastko R. Selmic
Date: 2025-05-15 00:00:00

This paper investigates the effect of constant time delay in weakly connected multi-agent systems modeled by double integrator dynamics. A novel analytical approach is proposed to establish an upper bound on the permissible time delay that ensures stability and consensus convergence. The analysis employs the Lambert W function method in higher-dimensional systems to derive explicit conditions under which consensus is achieved. The theoretical results are rigorously proven and provide insight into the allowable delay margins. The analysis applies to general leaderless undirected network topologies. The framework also accounts for complex and realistic delays, including non-commensurate communication delays. Numerical examples are provided to demonstrate the effectiveness of the proposed method.
The Path Integral Bottleneck: Exploring the Control-Compute Tradeoff
Authors: Justin Ting, Jing Shuang Li
Date: 2025-05-15 00:00:00

Executing a control sequence requires some computation effort. Intuitively, a high-effort, fine-grained computation should result in better control (e.g. lower cost), whereas little to no computation effort would lead to worse control. To quantify and explore the tradeoff between control performance and compute effort, we present the Path Integral Bottleneck (PIB), a fusion of the Path Integral (PI) optimal control and Information Bottleneck (IB) frameworks. Both frameworks provide flexible and probabilistic descriptions of control. The PI does not limit itself to a particular control law, and the IB is not bound to any specific state encoding. Combining the generality of both frameworks enables us to produce an analytical description of the control-compute tradeoff. We provide PIB formulations for both continuous and discrete random variables. With these formulations, we can plot a tradeoff curve between performance and computation effort for any given plant description and control cost function. Simulations of a cart-pole for both the continuous and discrete variable cases reveal fundamental control-compute tradeoffs, exposing regions where the task performance-per-compute is higher than others.
Dynamic Beam-Stabilized, Additive-Printed Flexible Antenna Arrays with On-Chip Rapid Insight Generation
Authors: Sreeni Poolakkal, Abdullah Islam, Arpit Rao, Shrestha Bansal, Ted Dabrowski, Kalsi Kwan, Zhongxuan Wang, Amit Kumar Mishra, Julio Navarro, Shenqiang Ren, John Williams, Sudip Shekhar, Subhanshu Gupta
Date: 2025-05-15 00:00:00

Conformal phased arrays promise shape-changing properties, multiple degrees of freedom to the scan angle, and novel applications in wearables, aerospace, defense, vehicles, and ships. However, they have suffered from two critical limitations. (1) Although most applications require on-the-move communication and sensing, prior conformal arrays have suffered from dynamic deformation-induced beam pointing errors. We introduce a Dynamic Beam-Stabilized (DBS) processor capable of beam adaptation through on-chip real-time control of fundamental gain, phase, and delay for each element. (2) Prior conformal arrays have leveraged additive printing to enhance flexibility, but conventional printable inks based on silver are expensive, and those based on copper suffer from spontaneous metal oxidation that alters trace impedance and degrades beamforming performance. We instead leverage a low-cost Copper Molecular Decomposition (CuMOD) ink with < 0.1% variation per degree C with temperature and strain and correct any residual deformity in real-time using the DBS processor. Demonstrating unified material and physical deformation correction, our CMOS DBS processor is low power, low-area, and easily scalable due to a tile architecture, thereby ideal for on-device implementations.
Measuring Flexibility through Reduction Potential
Authors: Polina Alexeenko, Matthew Bruchon, Jesse Bennett
Date: 2025-05-14 00:00:00

While electric vehicles (EVs) often exhibit substantial flexibility, harnessing this flexibility requires precise characterization of its timing and magnitude. This paper introduces the reduction potential matrix, a novel approach to EV load flexibility modeling which is both straightforward to calculate and intuitive to interpret. This paper demonstrates the approach by quantifying flexibility for two distinct commercial vehicle groups--freight vehicles and transit buses--using simulated charging data from Virginia. While both groups are found to have substantial flexibility, its properties vary across the groups. Naturally, this variability manifests in differences in each group's role as a grid resource. The paper concludes with a discussion on how system planners, fleet operators, and other stakeholders can use the matrix to assess and leverage EV flexibility.
Regulation without calibration
Authors: Rodolphe Sepulchre, Alessandro Cecconi, Michelangelo Bin, Lorenzo Marconi
Date: 2025-05-14 00:00:00

This article revisits the importance of the internal model principle in the literature of regulation and synchronization. Trajectory regulation, the task of regulating continuous-time signals generated by differential equations, is contrasted with event regulation, the task of only regulating discrete events associated with the trajectories. In trajectory regulation, the internal model principle requires an exact internal generator of the continuous-time trajectories, which translates into unrealistic calibration requirements. Event regulation is envisioned as a way to relieve calibration of the continuous behavior while ensuring reliability of the discrete events.
Coordinated Multi-Valve Disturbance-Rejection Pressure Control for High-Altitude Test Stands via Exterior Penalty Functions
Authors: Zhang Louyue, Li Xin, Zhai Chao, Shi Duoqi, Zhang Hehong, Dan Zhihong, Wang Xi, Liu Jiashuai, Xiao Gaoxi
Date: 2025-05-14 00:00:00

High altitude simulation test benches for aero engines employ multi chamber, multi valve intake systems that demand effective decoupling and strong disturbance rejection during transient tests. This paper proposes a coordinated active disturbance rejection control (ADRC) scheme based on an external penalty function. The chamber pressure safety limit is reformulated as an inequality constrained optimization problem, and an exponential penalty together with a gradient based algorithm is designed for dynamic constraint relaxation, with global convergence rigorously proven. A coordination term is then integrated into a distributed ADRC framework to yield a multi valve coordinated LADRC controller, whose asymptotic stability is established via Lyapunov theory. Hardware in the loop simulations using MATLAB/Simulink and a PLC demonstrate that, under $\pm$3 kPa pressure constraints, chamber V2's maximum error is 1.782 kPa (77.1\% lower than PID control), and under a 180 kg/s^2 flow rate disturbance, valve oscillations decrease from $\pm$27\% to $\pm$5\% (an 81.5\% reduction). These results confirm the proposed method's superior disturbance rejection and decoupling performance.
Data-driven Internal Model Control for Output Regulation
Authors: Wenjie Liu, Yifei Li, Jian Sun, Gang Wang, Keyou You, Lihua Xie, Jie Chen
Date: 2025-05-14 00:00:00
Tasks: LEMMA, model

Output regulation is a fundamental problem in control theory, extensively studied since the 1970s. Traditionally, research has primarily addressed scenarios where the system model is explicitly known, leaving the problem in the absence of a system model less explored. Leveraging the recent advancements in Willems et al.'s fundamental lemma, data-driven control has emerged as a powerful tool for stabilizing unknown systems. This paper tackles the output regulation problem for unknown single and multi-agent systems (MASs) using noisy data. Previous approaches have attempted to solve data-based output regulation equations (OREs), which are inadequate for achieving zero tracking error with noisy data. To circumvent the need for solving data-based OREs, we propose an internal model-based data-driven controller that reformulates the output regulation problem into a stabilization problem. This method is first applied to linear time-invariant (LTI) systems, demonstrating exact solution capabilities, i.e., zero tracking error, through solving a straightforward data-based linear matrix inequality (LMI). Furthermore, we extend our approach to solve the $k$th-order output regulation problem for nonlinear systems. Extensions to both linear and nonlinear MASs are discussed. Finally, numerical tests validate the effectiveness and correctness of the proposed controllers.
Leveraging Offline Data from Similar Systems for Online Linear Quadratic Control
Authors: Shivam Bajaj, Prateek Jaiswal, Vijay Gupta
Date: 2025-05-14 00:00:00
Tasks: Thompson Sampling

``Sim2real gap", in which the system learned in simulations is not the exact representation of the real system, can lead to loss of stability and performance when controllers learned using data from the simulated system are used on the real system. In this work, we address this challenge in the linear quadratic regulator (LQR) setting. Specifically, we consider an LQR problem for a system with unknown system matrices. Along with the state-action pairs from the system to be controlled, a trajectory of length $S$ of state-action pairs from a different unknown system is available. Our proposed algorithm is constructed upon Thompson sampling and utilizes the mean as well as the uncertainty of the dynamics of the system from which the trajectory of length $S$ is obtained. We establish that the algorithm achieves $\tilde{\mathcal{O}}({f(S,M_{\delta})\sqrt{T/S}})$ Bayes regret after $T$ time steps, where $M_{\delta}$ characterizes the \emph{dissimilarity} between the two systems and $f(S,M_{\delta})$ is a function of $S$ and $M_{\delta}$. When $M_{\delta}$ is sufficiently small, the proposed algorithm achieves $\tilde{\mathcal{O}}({\sqrt{T/S}})$ Bayes regret and outperforms a naive strategy which does not utilize the available trajectory.
Resource Allocation with Multi-Team Collaboration Based on Hamilton's Rule
Authors: Riwa Karam, Ruoyu Lin, Brooks A. Butler, Magnus Egerstedt
Date: 2025-05-13 00:00:00

This paper presents a multi-team collaboration strategy based on Hamilton's rule from ecology that facilitates resource allocation among multiple teams, where agents are considered as shared resource among all teams that must be allocated appropriately. We construct an algorithmic framework that allows teams to make bids for agents that consider the costs and benefits of transferring agents while also considering relative mission importance for each team. This framework is applied to a multi-team coverage control mission to demonstrate its effectiveness. It is shown that the necessary criteria of a mission evaluation function are met by framing it as a function of the locational coverage cost of each team with respect to agent gain and loss, and these results are illustrated through simulations.
To Stay or to Bypass: Unraveling Mainline Vehicles' Aggregate Strategic Decision-Making at Highway Weaving Ramps
Authors: Haohui He, Kexin Wang, Ruolin Li
Date: 2025-05-13 00:00:00
Tasks: Decision Making, Management

The weaving ramp scenario is a critical bottleneck in highway networks due to conflicting flows and complex interactions among merging, exiting, and through vehicles. In this work, we propose a game-theoretic model to capture and predict the aggregate lane choice behavior of mainline through vehicles as they approach the weaving zone. Faced with potential conflicts from merging and exiting vehicles, mainline vehicles can either bypass the conflict zone by changing to an adjacent lane or stay steadfast in their current lane. Our model effectively captures these strategic choices using a small set of parameters, requiring only limited traffic measurements for calibration. The model's validity is demonstrated through SUMO simulations, achieving high predictive accuracy. The simplicity and flexibility of the proposed framework make it a practical tool for analyzing bottleneck weaving scenarios and informing traffic management strategies.
High-throughput Screening of the Mechanical Properties of Peptide Assemblies
Authors: Sarah K. Yorke, Zhenze Yang, Aviad Levin, Alice Ray, Jeremy Owusu Boamah, Tuomas P. J. Knowles, Markus J. Buehler
Date: 2025-05-13 00:00:00

Peptides are recognized for their varied self-assembly behaviors, forming a wide array of structures and geometries, such as spheres, fibers, and hydrogels, each presenting a unique set of material properties. The functionalities of these materials hold exceptional interest for applications in biology, medicine, photonics, nanotechnology and the food industry. In specific, the ability to exploit peptides as viable and sustainable mechanical materials requires sequence design that enables superior performance, notably a high Young's modulus. As the peptide sequence space is vast, however, even a slight increase in sequence length leads to an exponential increase in the number of potential peptide sequences to be characterized. Here, we combine coarse-grained molecular dynamics simulations, atomic force microscopy experiments and machine learning models to correlate the sequence length and composition with the mechanical properties of self-assembled peptides. We calculate the Young's modulus for all possible amino acid sequences of di- and tripeptides using high-throughput coarse-grained methods, and validate these calculations through in-situ mechanical characterization. For pentapeptides, we select and calculate properties for a subset of sequences to train a machine learning model, which allows us to predict the modulus for other sequences. The combined workflow not only identifies promising peptide candidates with exceptional mechanical performances, but also extends current understanding of the sequence-to-function relationships for peptide materials, for specific applications.
Emergent dynamical phases and collective motion in termites
Authors: Leticia R. Paiva, Sidiney G. Alves, Og DeSouza, Octavio Miramontes
Date: 2025-04-28 00:00:00
Tasks: Clustering

Termites which are able to forage in the open can be often seen, in the field or in the lab: (i) wandering around, forming no observable pattern, or (ii) clustering themselves in a dense and almost immobile pack, or (iii) milling about in a circular movement. Despite been well reported patterns, they are normally regarded as independent phenomena whose specific traits have never been properly quantified. Evidence, however, favours the hypothesis that these are interdependent patterns, arisen from self-organised interactions and movement among workers. After all, termites are a form of active matter where blind cooperative individuals are self-propelled and lack the possibility of visual cues to spatially orientate and align. It follows that their non-trivial close-contact patterns could generate motion-collision induced phase separations. This would then trigger the emergence of these three patterns (disorder, clustering, milling) as parts of the same continuum. By inspecting termite groups confined in arenas, we could quantitatively describe each one of these patterns in detail. We identified disorder, clustering and milling spatial patterns. These phases and their transitions are characterised aiming to offer refinements in the understanding of these aspects of self-propelled particles in active matter where close-range contacts and collisions are important.
Temporal Interception and Present Reconstruction: A Cognitive-Signal Model for Human and AI Decision Making
Authors: Carmel Mary Esther A
Date: 2025-05-11 00:00:00
Tasks: Decision Making

This paper proposes a novel theoretical model to explain how the human mind and artificial intelligence can approach real-time awareness by reducing perceptual delays. By investigating cosmic signal delay, neurological reaction times, and the ancient cognitive state of stillness, we explore how one may shift from reactive perception to a conscious interface with the near future. This paper introduces both a physical and cognitive model for perceiving the present not as a linear timestamp, but as an interference zone where early-arriving cosmic signals and reactive human delays intersect. We propose experimental approaches to test these ideas using human neural observation and neuro-receptive extensions. Finally, we propose a mathematical framework to guide the evolution of AI systems toward temporally efficient, ethically sound, and internally conscious decision-making processes
Monte-Carlo Option Pricing in Quantum Parallel
Authors: Robert Scriba, Yuying Li, Jingbo B Wang
Date: 2025-05-14 00:00:00

Financial derivative pricing is a significant challenge in finance, involving the valuation of instruments like options based on underlying assets. While some cases have simple solutions, many require complex classical computational methods like Monte Carlo simulations and numerical techniques. However, as derivative complexities increase, these methods face limitations in computational power. Cases involving Non-Vanilla Basket pricing, American Options, and derivative portfolio risk analysis need extensive computations in higher-dimensional spaces, posing challenges for classical computers. Quantum computing presents a promising avenue by harnessing quantum superposition and entanglement, allowing the handling of high-dimensional spaces effectively. In this paper, we introduce a self-contained and all-encompassing quantum algorithm that operates without reliance on oracles or presumptions. More specifically, we develop an effective stochastic method for simulating exponentially many potential asset paths in quantum parallel, leading to a highly accurate final distribution of stock prices. Furthermore, we demonstrate how this algorithm can be extended to price more complex options and analyze risk within derivative portfolios.
Optimal Post-Hoc Theorizing
Authors: Andrew Y. Chen
Date: 2025-05-15 00:00:00

For many economic questions, the empirical results are not interesting unless they are strong. For these questions, theorizing before the results are known is not always optimal. Instead, the optimal sequencing of theory and empirics trades off a ``Darwinian Learning'' effect from theorizing first with a ``Statistical Learning'' effect from examining the data first. This short paper formalizes the tradeoff in a Bayesian model. In the modern era of mature economic theory and enormous datasets, I argue that post hoc theorizing is typically optimal.
Better Understanding Triple Differences Estimators
Authors: Marcelo Ortiz-Villavicencio, Pedro H. C. Sant'Anna
Date: 2025-05-15 00:00:00
Tasks: valid

Triple Differences (DDD) designs are widely used in empirical work to relax parallel trends assumptions in Difference-in-Differences (DiD) settings. This paper shows that common DDD implementations -- such as taking the difference between two DiDs or applying three-way fixed effects regressions -- are generally invalid when identification requires conditioning on covariates. In staggered adoption settings, the common DiD practice of pooling all not-yet-treated units as a comparison group introduces additional bias, even when covariates are not required for identification. These insights challenge conventional empirical strategies and underscore the need for estimators tailored specifically to DDD structures. We develop regression adjustment, inverse probability weighting, and doubly robust estimators that remain valid under covariate-adjusted DDD parallel trends. For staggered designs, we show how to correctly leverage multiple comparison groups to get more informative inference. Simulations highlight substantial bias reductions and precision gains relative to standard approaches, offering a new framework for credible DDD estimation in empirical research.