pwc-archive/papers-with-abstracts huggingface dataset:
Impact of Power Fluctuations on Frequency Quality
Authors: Angel Vaca, Federico Milano
Date: 2025-05-18 00:00:00

This paper analyzes how power injections affect frequency quality in power systems. We first derive a general expression linking active and reactive power injections at buses to the system's frequency. This formulation explicitly considers both real and imaginary frequency components, providing a complete description of frequency behavior in power systems during transients. Next, we extend our analysis to incorporate stochastic variations of power injections. Using the frequency divider concept and power-based frequency estimation, we develop analytical relationships linking stochastic load fluctuations to frequency deviations. We discuss under which conditions the Central Limit Theorem cannot be applied to capture the frequency distribution, thereby clarifying how its hypotheses are not satisfied in power system applications. Then, we establish clear criteria for the appropriate use of statistical methods in frequency analysis. Finally, we validate our theoretical results through simulations on modified IEEE 14-bus and all-island Irish transmission test systems, highlighting the accuracy, practical utility, and limitations of our proposed formulation.
Optimal Satellite Maneuvers for Spaceborne Jamming Attacks
Authors: Filippos Fotiadis, Quentin Rommel, Brian M. Sadler, Ufuk Topcu
Date: 2025-05-17 00:00:00
Tasks: Position

Satellites are becoming exceedingly critical for communication, making them prime targets for cyber-physical attacks. We consider a rogue satellite in low Earth orbit that jams the uplink communication between another satellite and a ground station. To achieve maximal interference with minimal fuel consumption, the jammer carefully maneuvers itself relative to the target satellite's antenna. We cast this maneuvering objective as a two-stage optimal control problem, involving i) repositioning to an efficient jamming position before uplink communication commences; and ii) maintaining an efficient jamming position after communication has started. We obtain the optimal maneuvering trajectories for the jammer and perform simulations to show how they enable the disruption of uplink communication with reasonable fuel consumption.
Particle Filtering for Enhanced Parameter Estimation in Bilinear Systems Under Colored Noise
Authors: Khalid Abd El Mageed Hag Elamin
Date: 2025-05-17 00:00:00
Tasks: parameter estimation

This paper addresses the challenging problem of parameter estimation in bilinear systems under colored noise. A novel approach, termed B-PF-RLS, is proposed, combining a particle filter (PF) with a recursive least squares (RLS) estimator. The B-PF-RLS algorithm tackles the complexities arising from system nonlinearities and colored noise by effectively estimating unknown system states using the particle filter, which are then integrated into the RLS parameter estimation process. Furthermore, the paper introduces an enhanced particle filter that eliminates the need for explicit knowledge of the measurement noise variance, enhancing the method's practicality for real-world applications. Numerical examples demonstrate the B-PF-RLS algorithm's superior performance in accurately estimating both system parameters and states, even under uncertain noise conditions. This work offers a robust and effective solution for system identification in various engineering applications involving bilinear models subject to complex noise environments.
Reach-avoid games for players with damped double integrator dynamics
Authors: Mengxin Lyu, Ruiliang Deng, Zongying Shi, Yisheng Zhong
Date: 2025-05-17 00:00:00

This paper studies a reach-avoid game of two damped double integrator players. An attacker aims to reach a static target, while a faster defender tries to protect the target by intercepting the attacker before it reaches the target. In scenarios where the defender succeeds, the defender aims to maximize the attacker's final distance from the target, while the attacker aims to minimize it. This work focuses on determining the equilibrium strategy in the defender-winning scenarios. The optimal state feedback strategy is obtained by a differential game approach combining geometric analysis. We construct a multiple reachable region to analyse the damped double integrator player's motion under optimal strategy. Building on this, a new type of the attacker's dominance region is introduced for the first time. It is shown that different strategies are required when the terminal point lies in distinct areas of the attacker's dominance region. Then, a necessary condition is derived for the proposed strategy to be optimal using differential game approach. Furthermore, a case where both players start at rest is discussed, and some useful properties about the dominance region and the optimal strategy are presented. Simulations are conducted to show the effectiveness of the proposed strategy.
PyScrew: A Comprehensive Dataset Collection from Industrial Screw Driving Experiments
Authors: Nikolai West, Jochen Deuse
Date: 2025-05-17 00:00:00
Tasks: Anomaly Detection, Friction

This paper presents a comprehensive collection of industrial screw driving datasets designed to advance research in manufacturing process monitoring and quality control. The collection comprises six distinct datasets with over 34,000 individual screw driving operations conducted under controlled experimental conditions, capturing the multifaceted nature of screw driving processes in plastic components. Each dataset systematically investigates specific aspects: natural thread degradation patterns through repeated use (s01), variations in surface friction conditions including contamination and surface treatments (s02), diverse assembly faults with up to 27 error types (s03-s04), and fabrication parameter variations in both upper and lower workpieces through modified injection molding settings (s05-s06). We detail the standardized experimental setup used across all datasets, including hardware specifications, process phases, and data acquisition methods. The hierarchical data model preserves the temporal and operational structure of screw driving processes, facilitating both exploratory analysis and the development of machine learning models. To maximize accessibility, we provide dual access pathways: raw data through Zenodo with a persistent DOI, and a purpose-built Python library (PyScrew) that offers consistent interfaces for data loading, preprocessing, and integration with common analysis workflows. These datasets serve diverse research applications including anomaly detection, predictive maintenance, quality control system development, feature extraction methodology evaluation, and classification of specific error conditions. By addressing the scarcity of standardized, comprehensive datasets in industrial manufacturing, this collection enables reproducible research and fair comparison of analytical approaches in an area of growing importance for industrial automation.
Model-free Dynamic Mode Adaptive Control using Matrix RLS
Authors: Parham Oveissi, Ankit Goel
Date: 2025-05-17 00:00:00

This paper presents a novel, model-free, data-driven control synthesis technique known as dynamic mode adaptive control (DMAC) for synthesizing controllers for complex systems whose mathematical models are not suitable for classical control design. DMAC consists of a dynamics approximation module and a controller module. The dynamics approximation module is motivated by data-driven reduced-order modeling techniques and directly approximates the system's dynamics in state-space form using a matrix version of the recursive least squares algorithm. The controller module includes an output tracking controller that utilizes sparse measurements from the system to generate the control signal. The DMAC controller design technique is demonstrated through various dynamic systems commonly found in engineering applications. A systematic sensitivity study demonstrates the robustness of DMAC with respect to its own hyperparameters and the system's parameters.
Data-based control of Logical Networks
Authors: Giorgia Disarò, Maria Elena Valcher
Date: 2025-05-16 00:00:00

In recent years, data-driven approaches have become increasingly pervasive across all areas of control engineering. However, the applications of data-based techniques to Boolean Control Networks (BCNs) are still very limited. In this paper we aim to fill this gap, by exploring the possibility of solving three fundamental control problems, i.e., state feedback stabilization, safe control and output regulation, for a BCN, leveraging only a limited amount of data generated by the network, without knowing or identifying its model.
Formal Uncertainty Propagation for Stochastic Dynamical Systems with Additive Noise
Authors: Steven Adams, Eduardo Figueiredo, Luca Laurenti
Date: 2025-05-16 00:00:00
Tasks: Quantization, Stochastic Optimization

In this paper, we consider discrete-time non-linear stochastic dynamical systems with additive process noise in which both the initial state and noise distributions are uncertain. Our goal is to quantify how the uncertainty in these distributions is propagated by the system dynamics for possibly infinite time steps. In particular, we model the uncertainty over input and noise as ambiguity sets of probability distributions close in the $\rho$-Wasserstein distance and aim to quantify how these sets evolve over time. Our approach relies on results from quantization theory, optimal transport, and stochastic optimization to construct ambiguity sets of distributions centered at mixture of Gaussian distributions that are guaranteed to contain the true sets for both finite and infinite prediction time horizons. We empirically evaluate the effectiveness of our framework in various benchmarks from the control and machine learning literature, showing how our approach can efficiently and formally quantify the uncertainty in linear and non-linear stochastic dynamical systems.
Event disturbance rejection: a case study
Authors: Alessandro Cecconi, Michelangelo Bin, Rodolphe Sepulchre, Lorenzo Marconi
Date: 2025-05-16 00:00:00

This article introduces the problem of robust event disturbance rejection. Inspired by the design principle of linear output regulation, a control structure based on excitable systems is proposed. Unlike the linear case, contraction of the closed-loop system must be enforced through specific input signals. This induced contraction enables a steady-state analysis similar to the linear case. Thanks to the excitable nature of the systems, the focus shifts from precise trajectory tracking to the regulation of discrete events, such as spikes. The study emphasizes rejecting events rather than trajectories and demonstrates the robustness of the approach, even under mismatches between the controller and the exosystem. This work is a first step towards developing a design principle for event regulation.
LLM-Enhanced Symbolic Control for Safety-Critical Applications
Authors: Amir Bayat, Alessandro Abate, Necmiye Ozay, Raphael M. Jungers
Date: 2025-05-16 00:00:00

Motivated by Smart Manufacturing and Industry 4.0, we introduce a framework for synthesizing Abstraction-Based Controller Design (ABCD) for reach-avoid problems from Natural Language (NL) specifications using Large Language Models (LLMs). A Code Agent interprets an NL description of the control problem and translates it into a formal language interpretable by state-of-the-art symbolic control software, while a Checker Agent verifies the correctness of the generated code and enhances safety by identifying specification mismatches. Evaluations show that the system handles linguistic variability and improves robustness over direct planning with LLMs. The proposed approach lowers the barrier to formal control synthesis by enabling intuitive, NL-based task definition while maintaining safety guarantees through automated validation.
Beyond KL-divergence: Risk Aware Control Through Cross Entropy and Adversarial Entropy Regularization
Authors: Menno van Zutphen, Domagoj Herceg, Duarte J. Antunes
Date: 2025-05-16 00:00:00

While the idea of robust dynamic programming (DP) is compelling for systems affected by uncertainty, addressing worst-case disturbances generally results in excessive conservatism. This paper introduces a method for constructing control policies robust to adversarial disturbance distributions that relate to a provided empirical distribution. The character of the adversary is shaped by a regularization term comprising a weighted sum of (i) the cross-entropy between the empirical and the adversarial distributions, and (ii) the entropy of the adversarial distribution itself. The regularization weights are interpreted as the likelihood factor and the temperature respectively. The proposed framework leads to an efficient DP-like algorithm -- referred to as the minsoftmax algorithm -- to obtain the optimal control policy, where the disturbances follow an analytical softmax distribution in terms of the empirical distribution, temperature, and likelihood factor. It admits a number of control-theoretic interpretations and can thus be understood as a flexible tool for integrating complementary features of related control frameworks. In particular, in the linear model quadratic cost setting, with a Gaussian empirical distribution, we draw connections to the well-known $\mathcal{H}_{\infty}$-control. We illustrate our results through a numerical example.
Lifelong reinforcement learning for health-aware fast charging of lithium-ion batteries
Authors: Meng Yuan, Changfu Zou
Date: 2025-05-16 00:00:00
Tasks: Deep Reinforcement Learning

Fast charging of lithium-ion batteries remains a critical bottleneck for widespread adoption of electric vehicles and stationary energy storage systems, as improperly designed fast charging can accelerate battery degradation and shorten lifespan. In this work, we address this challenge by proposing a health-aware fast charging strategy that explicitly balances charging speed and battery longevity across the entire service life. The key innovation lies in establishing a mapping between anode overpotential and the state of health (SoH) of battery, which is then used to constrain the terminal charging voltage in a twin delayed deep deterministic policy gradient (TD3) framework. By incorporating this SoH-dependent voltage constraint, our designed deep learning method mitigates side reactions and effectively extends battery life. To validate the proposed approach, a high-fidelity single particle model with electrolyte is implemented in the widely adopted PyBaMM simulation platform, capturing degradation phenomena at realistic scales. Comparative life-cycle simulations against conventional CC-CV, its variants, and constant current-constant overpotential methods show that the TD3-based controller reduces overall degradation while maintaining competitively fast charge times. These results demonstrate the practical viability of deep reinforcement learning for advanced battery management systems and pave the way for future explorations of health-aware, performance-optimized charging strategies.
User-centric Vehicle-to-Grid Optimization with an Input Convex Neural Network-based Battery Degradation Model
Authors: Arghya Mallick, Georgios Pantazis, Mohammad Khosravi, Peyman Mohajerin Esfahani, Sergio Grammatico
Date: 2025-05-16 00:00:00

We propose a data-driven, user-centric vehicle-to-grid (V2G) methodology based on multi-objective optimization to balance battery degradation and V2G revenue according to EV user preference. Given the lack of accurate and generalizable battery degradation models, we leverage input convex neural networks (ICNNs) to develop a data-driven degradation model trained on extensive experimental datasets. This approach enables our model to capture nonconvex dependencies on battery temperature and time while maintaining convexity with respect to the charging rate. Such a partial convexity property ensures that the second stage of our methodology remains computationally efficient. In the second stage, we integrate our data-driven degradation model into a multi-objective optimization framework to generate an optimal smart charging profile for each EV. This profile effectively balances the trade-off between financial benefits from V2G participation and battery degradation, controlled by a hyperparameter reflecting the user prioritization of battery health. Numerical simulations show the high accuracy of the ICNN model in predicting battery degradation for unseen data. Finally, we present a trade-off curve illustrating financial benefits from V2G versus losses from battery health degradation based on user preferences and showcase smart charging strategies under realistic scenarios.
A User-centric Game for Balancing V2G Benefits with Battery Degradation of Electric Vehicles
Authors: Arghya Mallick, Georgios Pantazis, Peyman Mohajerin Esfahani, Sergio Grammatico
Date: 2025-05-16 00:00:00

We present a novel user centric vehicle to grid framework that enables electric vehicle users to balance the trade off between financial benefits from VtoG and battery health degradation based on individual preference signals.
A Virtual Admittance-Based Fault Current Limiting Method for Grid-Forming Inverters
Authors: Zaid Ibn Mahmood, Hantao Cui, Ying Zhang
Date: 2025-05-15 00:00:00

Inverter-based resources (IBRs) are a key component in the ongoing modernization of power systems, with grid-forming (GFM) inverters playing a central role. Effective fault current limiting is a major challenge to modernizing power systems through increased penetration of GFM inverters. Due to their voltage-source nature, GFM inverters offer no direct control over the output current and, therefore, are susceptible to high fault currents. This vulnerability is especially pronounced during large phase jumps, a condition overlooked by most fault current limiting methods. This paper proposes a hybrid fault current limiting method implemented through a virtual admittance by leveraging the advantages of two virtual impedance (VI)-based methods tailored for three-phase faults and phase jump disturbances. Electromagnetic transient simulations conducted in MATLAB-Simulink demonstrate the method's effectiveness across various disturbances, validating its potential in single-loop GFM structures.
Mesh Stability Guaranteed Rigid Body Networks Using Control and Topology Co-Design
Authors: Zihao Song, Shirantha Welikala, Panos J. Antsaklis, Hai Lin
Date: 2025-05-15 00:00:00

Merging and splitting are of great significance for rigid body networks in making such networks reconfigurable. The main challenges lie in simultaneously ensuring the compositionality of the distributed controllers and the mesh stability of the entire network. To this end, we propose a decentralized control and topology co-design method for rigid body networks, which enables flexible joining and leaving of rigid bodies without the need to redesign the controllers for the entire network after such maneuvers. We first provide a centralized linear matrix inequality (LMI)-based control and topology co-design optimization of the rigid body networks with a formal mesh stability guarantee. Then, these centralized mesh stability constraints are made decentralized by a proposed alternative set of sufficient conditions. Using these decentralized mesh stability constraints and Sylvester's criterion-based decentralization techniques, the said centralized LMI problem is equivalently broken down into a set of smaller decentralized LMI problems that can be solved at each rigid body, enabling flexible merging/splitting of rigid bodies. Finally, the effectiveness of the proposed co-design method is illustrated based on a specifically developed simulator and a comparison study with respect to a state-of-the-art method.
Control Invariant Sets for Neural Network Dynamical Systems and Recursive Feasibility in Model Predictive Control
Authors: Xiao Li, Tianhao Wei, Changliu Liu, Anouck Girard, Ilya Kolmanovsky
Date: 2025-05-15 00:00:00
Tasks: Autonomous Driving, Model Predictive Control

Neural networks are powerful tools for data-driven modeling of complex dynamical systems, enhancing predictive capability for control applications. However, their inherent nonlinearity and black-box nature challenge control designs that prioritize rigorous safety and recursive feasibility guarantees. This paper presents algorithmic methods for synthesizing control invariant sets specifically tailored to neural network based dynamical models. These algorithms employ set recursion, ensuring termination after a finite number of iterations and generating subsets in which closed-loop dynamics are forward invariant, thus guaranteeing perpetual operational safety. Additionally, we propose model predictive control designs that integrate these control invariant sets into mixed-integer optimization, with guaranteed adherence to safety constraints and recursive feasibility at the computational level. We also present a comprehensive theoretical analysis examining the properties and guarantees of the proposed methods. Numerical simulations in an autonomous driving scenario demonstrate the methods' effectiveness in synthesizing control-invariant sets offline and implementing model predictive control online, ensuring safety and recursive feasibility.
Stability Analysis of Soft Sensor-Based Control Systems--A Luré-Postnikov Approach Using a Novel Gated Recurrent Neural Network
Authors: Eric Hilgert, Andreas Schwung
Date: 2025-05-14 00:00:00

This article addresses certification of closed-loop stability when a soft-sensor based on a gated recurrent neural network operates in the feedback path of a nonlinear control system. The Hadamard gating used in standard GRU/LSTM cells is shown to violate the Lur\'e-Postnikov Lyapunov conditions of absolute-stability theory, leading to conservative analysis. To overcome this limitation, a modified architecture--termed the Lur\'e-Postnikov gated recurrent neural network (LP-GRNN)--is proposed; its affine update law is compatible with the Lur\'e-Postnikov framework while matching the prediction accuracy of vanilla GRU/LSTM models on the NASA CMAPSS benchmark. Embedding the LP-GRNN, the plant, and a saturated PI controller in a unified standard nonlinear operator form (SNOF) reduces the stability problem to a compact set of tractable linear matrix inequalities (LMIs) whose feasibility certifies global asymptotic stability. A linearized boiler case study illustrates the workflow and validates the closed-loop performance, thereby bridging modern soft-sensor design with formal stability guarantees.