pwc-archive/papers-with-abstracts huggingface dataset:
Scalable Graph Condensation with Evolving Capabilities
Authors: Shengbo Gong, Mohammad Hashemi, Juntong Ni, Carl Yang, Wei Jin
Date: 2025-02-24 00:00:00
Tasks: Clustering

Graph data has become a pivotal modality due to its unique ability to model relational datasets. However, real-world graph data continues to grow exponentially, resulting in a quadratic increase in the complexity of most graph algorithms as graph sizes expand. Although graph condensation (GC) methods have been proposed to address these scalability issues, existing approaches often treat the training set as static, overlooking the evolving nature of real-world graph data. This limitation leads to inefficiencies when condensing growing training sets. In this paper, we introduce GECC (Graph Evolving Clustering Condensation), a scalable graph condensation method designed to handle large-scale and evolving graph data. GECC employs a traceable and efficient approach by performing class-wise clustering on aggregated features. Furthermore, it can inherits previous condensation results as clustering centroids when the condensed graph expands, thereby attaining an evolving capability. This methodology is supported by robust theoretical foundations and demonstrates superior empirical performance. Comprehensive experiments show that GECC achieves better performance than most state-of-the-art graph condensation methods while delivering an around 1,000x speedup on large datasets.
Inversion-free feed-forward and feedback control of MSM based actuator with large non-smooth input hysteresis
Authors: Michael Ruderman, Gianluca Giostra, Matteo Sette
Date: 2025-02-25 00:00:00

Dynamic systems with a large and non-smooth hysteresis in the feedforward channel challenge the design of feedback control since the instantaneous input gain is varying during the operation, in the worst case between zero and infinity. Magnetic shape memory (MSM) actuators with multi-stable transitions represent such untypical system plant with only the output displacement being measured. This paper provides a case study of designing the feedforward and feedback control system for an MSM-based actuator setup with a fairly high level of the output sensing noise. First, the recently introduced inversion-free feedforward hysteresis compensator is adapted for the Krasnoselskii-Pokrovskii operator model. Then, a robust feedback proportional-integral (PI) loop shaping is performed, while taking into account the lagging behavior of the low-pass filtering and system uncertainties. Experimental results show that the parallel action of feedforward and feedback parts improves the overall performance of position control.
Off-Policy Temporal Difference Learning for Perturbed Markov Decision Processes: Theoretical Insights and Extensive Simulations
Authors: Ali Forootani, Raffaele Iervolino, Massimo Tipaldi, Mohammad Khosravi
Date: 2025-02-25 00:00:00

Dynamic Programming suffers from the curse of dimensionality due to large state and action spaces, a challenge further compounded by uncertainties in the environment. To mitigate these issue, we explore an off-policy based Temporal Difference Approximate Dynamic Programming approach that preserves contraction mapping when projecting the problem into a subspace of selected features, accounting for the probability distribution of the perturbed transition probability matrix. We further demonstrate how this Approximate Dynamic Programming approach can be implemented as a particular variant of the Temporal Difference learning algorithm, adapted for handling perturbations. To validate our theoretical findings, we provide a numerical example using a Markov Decision Process corresponding to a resource allocation problem.
A Complex-Valued Feedback Linearization-Based Controller for a Voltage Source Inverter Tied to the Grid via a Second-Order Filter
Authors: Gerardo Tapia-Otaegui, Jorge A. Solsona, Sebastian Gomez Jorge, Ana Susperregui, Claudio A. Busada, M. Itsaso Martínez
Date: 2025-02-25 00:00:00
Tasks: Unity

In this document, a nonlinear control law for a grid-tied converter is introduced. The converter topology consists of a voltage source inverter (VSI) linked to the grid through an inductive-capacitive second-order filter, its input being connected to a capacitive DC-link supplied by a renewable energy-based input power source. In order to achieve good performance in presence of large state excursions caused mainly by substantial set-point modifications and/or considerable disturbances, a nonlinear control law based on a complex-valued feedback linearization strategy is designed. Specifically, a flat output is adopted, which is given by the summation of the energy stored in the DC-link capacitor and in the output filter's inductor and capacitor, as well as by the reactive energy at the output. After linearizing the system through a pertinent coordinate transformation and a nonlinear feedback, a linear trajectory tracking control law is implemented. The performance of the system controlled by applying the proposed strategy is tested via simulation for a very weak grid of unity X/R ratio, yielding satisfactory results.
A Normal Variance Mixture Model for Robust Kalman Filtering
Authors: Michael J. Walsh
Date: 2025-02-25 00:00:00
Tasks: State Space Models

The Kalman filter is ubiquitous for state space models because of its desirable statistical properties, ease of implementation, and generally good performance. However, it can perform poorly in the presence of outliers, or measurements with noise variances much greater than those assumed by the filter. An algorithm that is similar to the Kalman filter but robust to outliers is derived in this report. This algorithm -- called the normal variance mixture filter (NVMF) -- replaces the Gaussian distribution for the noise in the Kalman filter measurement model with a normal variance mixture distribution that admits heavier tails. Choice of the mixing density determines the complexity and performance of the NVMF. When the mixing density is the Dirac delta function, the NVMF is equivalent to the Kalman filter. Choice of an inverse gamma mixing density leads to closed-form recursions for the state estimate and its error covariance matrix that are robust to outliers. The NVMF is compared to the benchmark probabilistic data association filter (PDAF), as well as two other robust filters from the recent literature, for a simulated example. While all four robust filters outperform the Kalman filter when outliers are present, the NVMF provides the most consistent performance across all simulations.
Sample-efficient diffusion-based control of complex nonlinear systems
Authors: Hongyi Chen, Jingtao Ding, Jianhai Shu, Xinchun Yu, Xiaojun Liang, Yong Li, Xiao-Ping Zhang
Date: 2025-02-25 00:00:00

Complex nonlinear system control faces challenges in achieving sample-efficient, reliable performance. While diffusion-based methods have demonstrated advantages over classical and reinforcement learning approaches in long-term control performance, they are limited by sample efficiency. This paper presents SEDC (Sample-Efficient Diffusion-based Control), a novel diffusion-based control framework addressing three core challenges: high-dimensional state-action spaces, nonlinear system dynamics, and the gap between non-optimal training data and near-optimal control solutions. Through three innovations - Decoupled State Diffusion, Dual-Mode Decomposition, and Guided Self-finetuning - SEDC achieves 39.5\%-49.4\% better control accuracy than baselines while using only 10\% of the training samples, as validated across three complex nonlinear dynamic systems. Our approach represents a significant advancement in sample-efficient control of complex nonlinear systems. The implementation of the code can be found at https://anonymous.4open.science/r/DIFOCON-C019.
Distributed Zonotopic Fusion Estimation for Multi-sensor Systems
Authors: Yuchen Zhang, Bo Chen, Zheming Wang, Wen-An Zhang, Li Yu, Lei Guo
Date: 2025-02-25 00:00:00

Fusion estimation is often used in multi-sensor systems to provide accurate state information which plays an important role in the design of efficient control and decision-making. This paper is concerned with the distributed zonotopic fusion estimation problem for multi-sensor systems. The objective is to propose a zonotopic fusion estimation approach using different zonotope fusion criteria. We begin by proposing a novel zonotope fusion criterion to compute a distributed zonotopic fusion estimate (DZFE). The DZFE is formulated as a zonotope enclosure for the intersection of local zonotopic estimates from individual sensors. Then, the optimal parameter matrices for tuning the DZFE are determined by the analytical solution of an optimization problem. To reduce the conservatism of the DZFE with optimal parameters, we enhance our approach with an improved zonotope fusion criterion, which further improves the estimation performance of this DZFE by constructing tight strips for the intersection. In addition, we tackle the problem of handling sequentially arrived local estimates in realistic communication environments with a sequential zonotope fusion criterion. This sequential zonotope fusion offers reduced computational complexity compared to batch zonotope fusion. Notice that the proposed zonotope fusion criteria are designed to meet the state inclusion property and demonstrate performance superiority over local zonotopic estimates. We also derive stability conditions for these DZFEs to ensure their generator matrices are ultimately bounded. Finally, two illustrative examples are employed to show the effectiveness and advantages of the proposed methods.
Data-Driven Input-Output Control Barrier Functions
Authors: Mohammad Bajelani, Klaske van Heusden
Date: 2025-02-24 00:00:00
Tasks: State Estimation, valid

Control Barrier Functions (CBFs) offer a framework for ensuring set invariance and designing constrained control laws. However, crafting a valid CBF relies on system-specific assumptions and the availability of an accurate system model, underscoring the need for systematic data-driven synthesis methods. This paper introduces a data-driven approach to synthesizing a CBF for discrete-time LTI systems using only input-output measurements. The method begins by computing the maximal control invariant set using an input-output data-driven representation, eliminating the need for precise knowledge of the system's order and explicit state estimation. The proposed CBF is then systematically derived from this set, which can accommodate multiple input-output constraints. Furthermore, the proposed CBF is leveraged to develop a minimally invasive safety filter that ensures recursive feasibility with an adaptive decay rate. To improve clarity, we assume a noise-free dataset, though the method can be extended using data-driven reachability to capture noise effects and handle uncertainty. Finally, the effectiveness of the proposed method is demonstrated on an unknown time-delay system.
CalibRefine: Deep Learning-Based Online Automatic Targetless LiDAR-Camera Calibration with Iterative and Attention-Driven Post-Refinement
Authors: Lei Cheng, Lihao Guo, Tianya Zhang, Tam Bang, Austin Harris, Mustafa Hajij, Mina Sartipi, Siyang Cao
Date: 2025-02-24 00:00:00
Tasks: Autonomous Driving, Camera Calibration, Sensor Fusion

Accurate multi-sensor calibration is essential for deploying robust perception systems in applications such as autonomous driving and intelligent transportation. Existing LiDAR-camera calibration methods often rely on manually placed targets, preliminary parameter estimates, or intensive data preprocessing, limiting their scalability and adaptability in real-world settings. In this work, we propose a fully automatic, targetless, and online calibration framework, CalibRefine, which directly processes raw LiDAR point clouds and camera images. Our approach is divided into four stages: (1) a Common Feature Discriminator that leverages relative spatial positions, visual appearance embeddings, and semantic class cues to identify and generate reliable LiDAR-camera correspondences, (2) a coarse homography-based calibration that uses the matched feature correspondences to estimate an initial transformation between the LiDAR and camera frames, serving as the foundation for further refinement, (3) an iterative refinement to incrementally improve alignment as additional data frames become available, and (4) an attention-based refinement that addresses non-planar distortions by leveraging a Vision Transformer and cross-attention mechanisms. Extensive experiments on two urban traffic datasets demonstrate that CalibRefine achieves high-precision calibration with minimal human input, outperforming state-of-the-art targetless methods and matching or surpassing manually tuned baselines. Our results show that robust object-level feature matching, combined with iterative refinement and self-supervised attention-based refinement, enables reliable sensor alignment in complex real-world conditions without ground-truth matrices or elaborate preprocessing. Code is available at https://github.com/radar-lab/Lidar_Camera_Automatic_Calibration
Gaussian Process-Based Scalar Field Estimation in GPS-Denied Environments
Authors: Muzaffar Qureshi, Tochukwu Elijah Ogri, Humberto Ramos, Zachary I. Bell, Rushikesh Kamalapurkar
Date: 2025-02-24 00:00:00

This paper presents a methodology for an autonomous agent to map an unknown scalar field in GPS-denied regions. To reduce localization errors, the agent alternates between GPS-enabled and GPS-denied areas while collecting measurements. User-defined error bounds determine the dwell time in each region. A switching trajectory is then designed to ensure field measurements in GPS-denied regions remain within the specified error limits. A Lyapunov-based stability analysis guarantees bounded error trajectories while tracking the desired path. The effectiveness of the proposed methodology is demonstrated through simulations, with an error analysis comparing the GP-predicted scalar field model to the actual field.
Distributed Coordination for Heterogeneous Non-Terrestrial Networks
Authors: Jikang Deng, Hui Zhou, Mohamed-Slim Alouini
Date: 2025-02-24 00:00:00
Tasks: Deep Reinforcement Learning, Scheduling

To guarantee global coverage and ubiquitous connectivity, the Non-terrestrial Network (NTN) technology has been regarded as a key enabling technology in the Six Generation (6G) network, which consists of the unmanned aerial vehicle (UAV), high-altitude platform (HAP), and satellite. It is noted that the unique characteristics of various NTN platforms directly impact the design and implementation of NTNs, which results in highly dynamic and heterogeneous networks. Even within the same tier, such as the space tier, the NTNs are developed based on different platforms including Low Earth Orbit (LEO), Medium Earth Orbit (MEO), and Geostationary Earth Orbit (GEO). Therefore, distributed coordination among heterogeneous NTNs remains an important challenge. Although distributed learning framework finds a wide range of applications by leveraging rich distributed data and computation resources. The explicit and systematic analysis of the individual layers' challenges, and corresponding distributed coordination solutions in heterogeneous NTNs has not been proposed yet. In this article, we first summarize the unique characteristics of each NTN platform, and analyze the corresponding impact on the design and implementation of the NTN. We then identify the communication challenges of heterogeneous NTNs in individual layers, where the potential coordinated solutions are identified. We further illustrate the multi-agent deep reinforcement learning (MADRL) algorithms tailored for coordinated solutions in heterogeneous NTNs. Last but not least, we present a case study of the user scheduling optimization problem in heterogeneous UAVs-based cellular networks, where the multi-agent deep deterministic policy gradient (MADDPG) technique is developed to validate the effectiveness of distributed coordination in heterogeneous NTNs.
Extremum Seeking Control for Antenna Pointing via Symmetric Product Approximation
Authors: Bo wang, Hashem Ashrafiuon, Sergey G. Nersesov
Date: 2025-02-24 00:00:00

This paper investigates extremum seeking control for a torque-controlled antenna pointing system without direct angular measurements. We consider a two-degree-of-freedom (2-DOF) antenna system that receives an unknown signal from its environment, where the signal strength varies with the orientation of the antenna. It is assumed that only real-time measurements of the signal are available. We develop an extremum seeking control strategy that enables the antenna to autonomously adjust its direction to maximize the received signal strength based on the symmetric product approximation. Under suitable assumptions on the signal function, we prove local practical uniform asymptotic stability for the closed-loop system.
Rotatable Antenna Enabled Wireless Communication System with Visual Recognition: A Prototype Implementation
Authors: Liang Dai, Beixiong Zheng, Yanhua Tan, Lipeng Zhu, Fangjiong Chen, Rui Zhang
Date: 2025-02-24 00:00:00

Rotatable antenna (RA) is an emerging technology that has great potential to exploit additional spatial degrees of freedom (DoFs) by flexibly altering the three-dimensional (3D) orientation/boresight of each antenna. In this demonstration, we present a prototype of the RA-enabled wireless communication system with a visual recognition module to evaluate the performance gains provided by the RA in practical environments. In particular, a mechanically-driven RA is developed by integrating a digital servo motor, a directional antenna, and a microcontroller, which enables the dynamic adjustment of the RA orientation. Moreover, the orientation adjustment of the RA is guided by the user's direction information provided by the visual recognition module, thereby significantly enhancing system response speed and self-orientation accuracy. The experimental results demonstrate that the RA-enabled communication system achieves significant improvement in communication coverage performance compared to the conventional fixed antenna system.
On Space-Filling Input Design for Nonlinear Dynamic Model Learning: A Gaussian Process Approach
Authors: YuHan Liu, Máté Kiss, Roland Tóth, Maarten Schoukens
Date: 2025-02-24 00:00:00
Tasks: Efficient Exploration

While optimal input design for linear systems has been well-established, no systematic approach exists for nonlinear systems where robustness to extrapolation/interpolation errors is prioritized over minimizing estimated parameter variance. To address this issue, we develop a novel space-filling input design strategy for nonlinear system identification that ensures data coverage of a given region of interest. By placing a Gaussian Process (GP) prior on the joint input-state space, the proposed strategy leverages the GP posterior variance to construct a cost function that promotes space-filling input design. Consequently, this enables maximization of the coverage in the region of interest, thereby facilitating the generation of informative datasets. Furthermore, we theoretically prove that minimization of the cost function implies the space-filling property of the obtained data. Effectiveness of the proposed strategy is demonstrated on both an academic and a mass-spring-damper example, highlighting its potential practical impact on efficient exploration of the dynamics of nonlinear systems.
Engineering and Validating Cyber-Physical Energy Systems: Needs, Status Quo, and Research Trends
Authors: Thomas I. Strasser, Filip Pröstl Andrén
Date: 2025-02-24 00:00:00

A driving force for the realization of a sustainable energy supply is the integration of renewable energy resources. Due to their stochastic generation behaviour, energy utilities are confronted with a more complex operation of the underlying power grids. Additionally, due to technology developments, controllable loads, integration with other energy sources, changing regulatory rules, and the market liberalization, the systems operation needs adaptation. Proper operational concepts and intelligent automation provide the basis to turn the existing power system into an intelligent entity, a cyber-physical energy system. The electric energy system is therefore moving from a single system to a system of systems. While reaping the benefits with new intelligent behaviors, it is expected that system-level developments, architectural concepts, advanced automation and control as well as the validation and testing will play a significantly larger role in realizing future solutions and technologies. The implementation and deployment of these complex systems of systems are associated with increasing engineering complexity resulting also in increased engineering costs. Proper engineering and validation approaches, concepts, and tools are partly missing until now. Therefore, this paper discusses and summarizes the main needs and requirements as well as the status quo in research and development related to the engineering and validation of cyber-physical energy systems. Also research trends and necessary future activities are outlined.
Enhancing sensor attack detection in supervisory control systems modeled by probabilistic automata
Authors: Parastou Fahim, Samuel Oliveira, Rômulo Meira-Góes
Date: 2025-02-23 00:00:00
Tasks: Intrusion Detection

Sensor attacks compromise the reliability of cyber-physical systems (CPSs) by altering sensor outputs with the objective of leading the system to unsafe system states. This paper studies a probabilistic intrusion detection framework based on $\lambda$-sensor-attack detectability ($\lambda$-sa), a formal measure that evaluates the likelihood of a system being under attack based on observed behaviors. Our framework enhances detection by extending its capabilities to identify multiple sensor attack strategies using probabilistic information, which enables the detection of sensor attacks that were undetected by current detection methodologies. We develop a polynomial-time algorithm that verifies $\lambda$-sa detectability by constructing a weighted verifier automaton and solving the shortest path problem. Additionally, we propose a method to determine the maximum detection confidence level ($\lambda$*) achievable by the system, ensuring the highest probability of identifying attack-induced behaviors.
Least Squares based Estimation of Thevenin Equivalent in Noisy Distribution Grid
Authors: Taha Saeed Khan
Date: 2025-02-23 00:00:00

This work presents a novel approach that synergizes the extremum seeking method with an online least squares estimation technique to accurately estimate Thevenin equivalent circuit being seen at each node in distribution grids. Thevenin's theorem offers a simplified representation of electrical networks, critical for the effective monitoring, control, and optimization of grid operations. However, real-time identification of Thevenin parameters, particularly impedance, poses significant challenges due to the dynamic nature of distribution grids. By integrating extremum seeking algorithms, which are adept at locating optima in dynamic systems without explicit model information, with the robustness of least squares estimation, we develop a novel methodology that continuously adapts to grid fluctuations. These fusion harnesses the strengths of both techniques: the extremum seeking method's non-model-based optimization capabilities and the least squares method's proficiency in estimating parameter value in a noisy environment. The result is a robust, adaptive algorithm capable of delivering reliable Thevenin parameter estimations in real-time. Our simulation results demonstrate the efficacy of the proposed method, showcasing its potential as a tool for enhanced grid management and resilience.