pwc-archive/papers-with-abstracts huggingface dataset:
A 10.8mW Mixed-Signal Simulated Bifurcation Ising Solver using SRAM Compute-In-Memory with 0.6us Time-to-Solution
Authors: Alana Marie Dee, Sajjad Moazeni
Date: 2025-04-14 00:00:00
Tasks: Combinatorial Optimization

Combinatorial optimization problems are funda- mental for various fields ranging from finance to wireless net- works. This work presents a simulated bifurcation (SB) Ising solver in CMOS for NP-hard optimization problems. Analog domain computing led to a superior implementation of this algorithm as inherent and injected noise is required in SB Ising solvers. The architecture novelties include the use of SRAM compute-in-memory (CIM) to accelerate bifurcation as well as the generation and injection of optimal decaying noise in the analog domain. We propose a novel 10-T SRAM cell capable of performing ternary multiplication. When measured with 60- node, 50% density, random, binary MAXCUT graphs, this all- to-all connected Ising solver reliably achieves above 93% of the ground state solution in 0.6us with 10.8mW average power in TSMC 180nm CMOS. Our chip achieves an order of magnitude improvement in time-to-solution and power compared to previously proposed Ising solvers in CMOS and other platforms.
Reactive power flow optimization in AC drive systems
Authors: Sanjay Chandrasekaran, Catalin Arghir, Pieder Joerg, Florian Doerfler, Silvia Mastellone
Date: 2025-04-14 00:00:00

This paper explores a limit avoidance approach in the case of input (modulation) and output (current) constraints with the aim of enhancing system availability of AC drives. Drawing on the observation that, in a certain range of reactive power, there exists a trade-off between current and modulation magnitude, we exploit this freedom and define a constrained optimization problem. We propose two approaches, one in the form of an activation-function which drives the reactive power set-point towards safety, and an approach which uses online feedback optimization to set the reactive power dynamically. Both methods compromise reactive power tracking accuracy for increased system robustness. Through a high fidelity simulation, we compare the benefits of the two methods, highlighting their effectiveness in industrial applications.
A New Paradigm in IBR Modeling for Power Flow and Short Circuit Analysis
Authors: Zahid Javid, Firdous Ul Nazir, Wentao Zhu, Diptargha Chakravorty, Ahmed Aboushady, Mohamed Galeela
Date: 2025-04-14 00:00:00

The fault characteristics of inverter-based resources (IBRs) are different from conventional synchronous generators. The fault response of IBRs is non-linear due to saturation states and mainly determined by fault ride through (FRT) strategies of the associated voltage source converter (VSC). This results in prohibitively large solution times for power flows considering these short circuit characteristics, especially when the power system states change fast due to uncertainty in IBR generations. To overcome this, a phasor-domain steady state (SS) short circuit (SC) solver for IBR dominated power systems is proposed in this paper, and subsequently the developed IBR models are incorporated with a novel Jacobian-based Power Flow (PF) solver. In this multiphase PF solver, any power system components can be modeled by considering their original non-linear or linear mathematical representations. Moreover, two novel FRT strategies are proposed to fully utilize the converter capacity and to comply with IEEE-2800 2022 std and German grid code. The results are compared with the Electromagnetic Transient (EMT) simulation on the IEEE 34 test network and the 120 kV EPRI benchmark system. The developed IBR sequence domain PF model demonstrates more accurate behavior compared to the classical IBR generator model. The error in calculating the short circuit current with the proposed SC solver is less than 3%, while achieving significant speed improvements of three order of magnitudes.
Exploiting Structure in MIMO Scaled Graph Analysis
Authors: Timo de Groot, Tom Oomen, Sebastiaan van den Eijnden
Date: 2025-04-14 00:00:00

Scaled graphs offer a graphical tool for analysis of nonlinear feedback systems. Although recently substantial progress has been made in scaled graph analysis, at present their use in multivariable feedback systems is limited by conservatism. In this paper, we aim to reduce this conservatism by introducing multipliers and exploit system structure in the analysis with scaled graphs. In particular, we use weighted inner products to arrive at a weighted scaled graph and combine this with a commutation property to formulate a stability result for multivariable feedback systems. We present a method for computing the weighted scaled graph of Lur'e systems based on solving sets of linear matrix inequalities, and demonstrate a significant reduction in conservatism through an example.
Gradient modelling of memristive systems
Authors: Fulvio Forni, Rodolphe Sepulchre
Date: 2025-04-14 00:00:00

We introduce a gradient modeling framework for memristive systems. Our focus is on memristive systems as they appear in neurophysiology and neuromorphic systems. Revisiting the original definition of Chua, we regard memristive elements as gradient operators of quadratic functionals with respect to a metric determined by the memristance. We explore the consequences of gradient properties for the analysis and design of neuromorphic circuits.
Fully-Adaptive and Semi-Adaptive Frequency Sweep Algorithm Exploiting Loewner-State Model for EM Simulation of Multiport Systems
Authors: Shilpa T. N., Rakesh Sinha
Date: 2025-04-14 00:00:00

This paper employs a fully adaptive and semi-adaptive frequency sweep algorithm using the Loewner matrix-based state model for the electromagnetic simulation. The proposed algorithms use two Loewner matrix models with different or the same orders with small frequency perturbation for adaptive frequency selection. The error between the two models is calculated in each iteration, and the next frequency points are selected to minimize maximum error. With the help of memory, the algorithm terminates when the error between the model and the simulation result is reached within the specified error tolerance. In the fully adaptive frequency sweep algorithm, the method starts with the minimum and maximum frequency of simulation. In the semi-adaptive algorithm, a novel approach has been proposed to determine the initial number of frequency points necessary for system interpolation based on the electrical size of the structure. The proposed algorithms have been compared with the Stoer-Bulirsch algorithm and Pradovera's minimal sampling algorithm for electromagnetic simulation. Four examples are presented using MATLAB R2024b. The results show that the proposed methods offer better performance in terms of speed, accuracy and the requirement of the minimum number of frequency samples. The proposed method shows remarkable consistency with full-wave simulation data, and the algorithm can be effectively applicable to electromagnetic simulations.
Markov Clustering based Fully Automated Nonblocking Hierarchical Supervisory Control of Large-Scale Discrete-Event Systems
Authors: Yingying Liu, Zhaojian Cai, Kai Cai
Date: 2025-04-14 00:00:00
Tasks: Clustering

In this paper we revisit the abstraction-based approach to synthesize a hierarchy of decentralized supervisors and coordinators for nonblocking control of large-scale discrete-event systems (DES), and augment it with a new clustering method for automatic and flexible grouping of relevant components during the hierarchical synthesis process. This method is known as Markov clustering, which not only automatically performs grouping but also allows flexible tuning the sizes of the resulting clusters using a single parameter. Compared to the existing abstraction-based approach that lacks effective grouping method for general cases, our proposed approach based on Markov clustering provides a fully automated and effective hierarchical synthesis procedure applicable to general large-scale DES. Moreover, it is proved that the resulting hierarchy of supervisors and coordinators collectively achieves global nonblocking (and maximally permissive) controlled behavior under the same conditions as those in the existing abstraction-based approach. Finally, a benchmark case study is conducted to empirically demonstrate the effectiveness of our approach.
Modelling & Steady State Compliance Testing of an Improved Time Synchronized Phasor Measurement Unit Based on IEEE Standard C37.118.1
Authors: Nisarg Trivedi
Date: 2025-04-14 00:00:00
Tasks: Fault Detection

Synchrophasor technology is an emerging and developing technology for monitoring and control of wide area measurement systems (WAMS). In an elementary WAMS, two identical phasors measured at two different locations have difference in the phase angles measured since their reference waveforms are not synchronized with each other. Phasor measurement units (PMUs) measure input phasors with respect to a common reference wave based on the atomic clock pulses received from global positioning system (GPS) satellites, eliminating variation in the measured phase angles due to distant locations of the measurement nodes. This has found tremendous applications in quick fault detection, fault location analysis, accurate current, voltage, frequency and phase angle measurements in WAMS. Commercially available PMU models are often proven to be expensive for research and development as well as for grid integration projects. This research article proposes an economic PMU model optimized for accurate steadystate performance based on recursive discrete Fourier transform (DFT) and provides results and detailed analysis of the proposed PMU model as per the steady state compliance specifications of IEEE standard C37.118.1. Results accurate up to 13 digits after decimal point are obtained through the developed PMU model for both nominal and off-nominal frequency inputs in steady state.
Computationally Efficient State and Model Estimation via Interval Observers for Partially Unknown Systems
Authors: Mohammad Khajenejad, Zeyuan Jin
Date: 2025-04-14 00:00:00
Tasks: Computational Efficiency

This paper addresses the synthesis of interval observers for partially unknown nonlinear systems subject to bounded noise, aiming to simultaneously estimate system states and learn a model of the unknown dynamics. Our approach leverages Jacobian sign-stable (JSS) decompositions, tight decomposition functions for nonlinear systems, and a data-driven over-approximation framework to construct interval estimates that provably enclose the true augmented states. By recursively computing tight and tractable bounds for the unknown dynamics based on current and past interval framers, we systematically integrate these bounds into the observer design. Additionally, we formulate semi-definite programs (SDP) for observer gain synthesis, ensuring input-to-state stability and optimality of the proposed framework. Finally, simulation results demonstrate the computational efficiency of our approach compared to a method previously proposed by the authors.
Robust Output-Feedback MPC for Nonlinear Systems with Applications to Robotic Exploration
Authors: Scott Brown, Mohammad Khajenejad, Aamodh Suresh, Sonia Martinez
Date: 2025-04-14 00:00:00
Tasks: Model Predictive Control, Robot Navigation

This paper introduces a novel method for robust output-feedback model predictive control (MPC) for a class of nonlinear discrete-time systems. We propose a novel interval-valued predictor which, given an initial estimate of the state, produces intervals which are guaranteed to contain the future trajectory of the system. By parameterizing the control input with an initial stabilizing feedback term, we are able to reduce the width of the predicted state intervals compared to existing methods. We demonstrate this through a numerical comparison where we show that our controller performs better in the presence of large amounts of noise. Finally, we present a simulation study of a robot navigation scenario, where we incorporate a time-varying entropy term into the cost function in order to autonomously explore an uncertain area.
Hybrid Lyapunov and Barrier Function-Based Control with Stabilization Guarantees
Authors: Hugo Matias, Daniel Silvestre
Date: 2025-04-13 00:00:00

Control Lyapunov Functions (CLFs) and Control Barrier Functions (CBFs) can be combined, typically by means of Quadratic Programs (QPs), to design controllers that achieve performance and safety objectives. However, a significant limitation of this framework is the introduction of asymptotically stable equilibrium points besides the minimizer of the CLF, leading to deadlock situations even for simple systems and bounded convex unsafe sets. To address this problem, we propose a hybrid CLF-CBF control framework with global asymptotic stabilization and safety guarantees, offering a more flexible and systematic design methodology compared to current alternatives available in the literature. We further extend this framework to higher-order systems via a recursive procedure based on a joint CLF-CBF backstepping approach. The proposed solution is assessed through several simulation examples.
Learning-based decentralized control with collision avoidance for multi-agent systems
Authors: Omayra Yago Nieto, Alexandre Anahory Simoes, Juan I. Giribet, Leonardo J. Colombo
Date: 2025-04-13 00:00:00
Tasks: Collision Avoidance, Gaussian Processes

In this paper, we present a learning-based tracking controller based on Gaussian processes (GP) for collision avoidance of multi-agent systems where the agents evolve in the special Euclidean group in the space SE(3). In particular, we use GPs to estimate certain uncertainties that appear in the dynamics of the agents. The control algorithm is designed to learn and mitigate these uncertainties by using GPs as a learning-based model for the predictions. In particular, the presented approach guarantees that the tracking error remains bounded with high probability. We present some simulation results to show how the control algorithm is implemented.
Simultaneous Input and State Estimation under Output Quantization: A Gaussian Mixture approach
Authors: Rodrigo A. González, Angel L. Cedeño
Date: 2025-04-13 00:00:00
Tasks: Fault Detection, Quantization, State Estimation

Simultaneous Input and State Estimation (SISE) enables the reconstruction of unknown inputs and internal states in dynamical systems, with applications in fault detection, robotics, and control. While various methods exist for linear systems, extensions to systems with output quantization are scarce, and formal connections to limit Kalman filters in this context are lacking. This work addresses these gaps by proposing a novel SISE algorithm for linear systems with quantized output measurements that is based on a Gaussian mixture model formulation. The observation model is represented as a Gaussian sum density, leading to closed-form recursive equations in the form of a Gaussian sum filter. In the absence of input prior knowledge, the recursions converge to a limit-case SISE algorithm, implementable as a bank of linear SISE filters running in parallel. A simulation example is presented to illustrate the effectiveness of the proposed approach.
Nonlinear Online Optimization for Vehicle-Home-Grid Integration including Household Load Prediction and Battery Degradation
Authors: Francesco Popolizio, Torsten Wik, Chih Feng Lee, Changfu Zou
Date: 2025-04-13 00:00:00
Tasks: energy management, energy trading, Management

This paper investigates the economic impact of vehicle-home-grid integration, by proposing an online energy management algorithm that optimizes energy flows between an electric vehicle (EV), a household, and the electrical grid. The algorithm leverages vehicle-to-home (V2H) for self-consumption and vehicle-to-grid (V2G) for energy trading, adapting to real-time conditions through a hybrid long short-term memory (LSTM) neural network for accurate household load prediction, alongside a comprehensive nonlinear battery degradation model accounting for both cycle and calendar aging. Simulation results reveal significant economic advantages: compared to smart unidirectional charging, the proposed method yields an annual economic benefit of up to EUR 3046.81, despite a modest 1.96% increase in battery degradation. Even under unfavorable market conditions, where V2G energy selling generates no revenue, V2H alone ensures yearly savings of EUR 425.48. A systematic sensitivity analysis investigates how variations in battery capacity, household load, and price ratios affect economic outcomes, confirming the consistent benefits of bidirectional energy exchange. These findings highlight the potential of EVs as active energy nodes, enabling sustainable energy management and cost-effective battery usage in real-world conditions.
Ensemble-Enhanced Graph Autoencoder with GAT and Transformer-Based Encoders for Robust Fault Diagnosis
Authors: Moirangthem Tiken Singh
Date: 2025-04-13 00:00:00
Tasks: Dynamic Time Warping, Fault Diagnosis

Fault classification in industrial machinery is vital for enhancing reliability and reducing downtime, yet it remains challenging due to the variability of vibration patterns across diverse operating conditions. This study introduces a novel graph-based framework for fault classification, converting time-series vibration data from machinery operating at varying horsepower levels into a graph representation. We utilize Shannon's entropy to determine the optimal window size for data segmentation, ensuring each segment captures significant temporal patterns, and employ Dynamic Time Warping (DTW) to define graph edges based on segment similarity. A Graph Auto Encoder (GAE) with a deep graph transformer encoder, decoder, and ensemble classifier is developed to learn latent graph representations and classify faults across various categories. The GAE's performance is evaluated on the Case Western Reserve University (CWRU) dataset, with cross-dataset generalization assessed on the HUST dataset. Results show that GAE achieves a mean F1-score of 0.99 on the CWRU dataset, significantly outperforming baseline models-CNN, LSTM, RNN, GRU, and Bi-LSTM (F1-scores: 0.94-0.97, p < 0.05, Wilcoxon signed-rank test for Bi-LSTM: p < 0.05) -- particularly in challenging classes (e.g., Class 8: 0.99 vs. 0.71 for Bi-LSTM). Visualization of dataset characteristics reveals that datasets with amplified vibration patterns and diverse fault dynamics enhance generalization. This framework provides a robust solution for fault diagnosis under varying conditions, offering insights into dataset impacts on model performance.
Appointed-Time Fault-Tolerant Control for Flexible Hypersonic Vehicles with Unmeasurable States Independent of Initial Errors
Authors: Tianlong Zhao, Fei Hao
Date: 2025-04-13 00:00:00

This article aims to derive a practical tracking control algorithm for flexible air-breathing hypersonic vehicles (FAHVs) with lumped disturbances, unmeasurable states and actuator failures. Based on the framework of the backstepping technique, an appointed-time fault-tolerant protocol independent of initial errors is proposed. Firstly, a new type of a state observer is constructed to reconstruct the unmeasurable states. Then, an error transformation function is designed to achieve prescribed performance control that does not depend on the initial tracking error. To deal with the actuator failures, practical fixed-time neural network observers are established to provide the estimation of the lumped disturbances. Finally, the proposed control strategy can ensure the practical fixed-time convergence of the closed-loop system, thereby greatly enhancing the transient performance. The proposed method addresses the challenges of ensuring real-time measurement accuracy for angle of attack and flight path angle in hypersonic vehicles, coupled with potential sudden actuator failures, effectively overcoming the drawback of prescribed performance control that requires knowledge of initial tracking errors. Some simulation results are provided to demonstrate the feasibility and the effectiveness of the proposed strategy
Expressivity of Quadratic Neural ODEs
Authors: Joshua Hanson, Maxim Raginsky
Date: 2025-04-13 00:00:00

This work focuses on deriving quantitative approximation error bounds for neural ordinary differential equations having at most quadratic nonlinearities in the dynamics. The simple dynamics of this model form demonstrates how expressivity can be derived primarily from iteratively composing many basic elementary operations, versus from the complexity of those elementary operations themselves. Like the analog differential analyzer and universal polynomial DAEs, the expressivity is derived instead primarily from the "depth" of the model. These results contribute to our understanding of what depth specifically imparts to the capabilities of deep learning architectures.
Modeling Scrap Composition in Electric Arc and Basic Oxygen Furnaces
Authors: Yiqing Zhou, Karsten Naert, Dirk Nuyens
Date: 2025-04-13 00:00:00
Tasks: ARC

This article aims to determine the composition of scrap (recycled material) used in an Electric Arc Furnace (EAF) or basic Oxygen Furnace (BOF) based on the assumption of mass balance. Accurate knowledge of this composition can increase the usage of recycled material to produce steel, reducing the need for raw ore extraction and minimizing environmental impact by conserving natural resources and lowering carbon emissions. The study develops two models to describe the behavior of elements in the EAF or BOF process. A linear state space model is used for elements transferring completely from scrap to steel, while a non-linear state space model is applied to elements moving into both steel and slag. The Kalman filter and unscented Kalman filter are employed to approximate these models, respectively. Importantly, the models leverage only data already collected as part of the standard production process, avoiding the need for additional measurements that are often costly. This article outlines the formulation of both models, the algorithms used, and discusses the hyperparameters involved. We provide practical suggestions on how to choose appropriate hyperparameters based on expert knowledge and historical data. The models are applied to real BOF data. Cu and Cr are chosen as examples for linear and non-linear models, respectively. The results show that both models can reconstruct the composition of scrap for these elements. The findings provide valuable insights for improving process control and ensuring product quality in steelmaking.
Computationally Efficient Signal Detection with Unknown Bandwidths
Authors: Ali Rasteh, Sundeep Rangan
Date: 2025-04-12 00:00:00

Signal detection in environments with unknown signal bandwidth and time intervals is a basic problem in adversarial and spectrum-sharing scenarios. This paper addresses the problem of detecting signals occupying unknown degrees of freedom from non-coherent power measurements where the signal is constrained to an interval in one dimension or hypercube in multiple dimensions. A Generalized Likelihood Ratio Test (GLRT) is derived, resulting in a straightforward metric involving normalized average signal energy on each candidate signal set. We present bounds on false alarm and missed detection probabilities, demonstrating their dependence on signal-to-noise ratios (SNR) and signal set sizes. To overcome the inherent computational complexity of exhaustive searches, we propose a computationally efficient binary search method, reducing the complexity from O(N2) to O(N) for one-dimensional cases. Simulations indicate that the method maintains performance near exhaustive searches and achieves asymptotic consistency, with interval-of-overlap converging to one under constant SNR as measurement size increases. The simulation studies also demonstrate superior performance and reduced complexity compared to contemporary neural network-based approaches, specifically outperforming custom-trained U-Net models in spectrum detection tasks.