Searcharxiv⌕ Search

SEARCH · Searcharxiv

Search Searcharxiv

Search indexed arXiv papers on artificial intelligence, large language models, computer vision and robotics. Read source abstracts and follow links to arXiv.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 271 records · Page 15Linked to original sources

Stability of Droop-Controlled Low-Frequency Transmission Lines

Low-Frequency AC (LFAC) transmission systems employing power converters are being considered for a varity of applications. This work studies the small-signal stability of an LFAC transmission line controlled by the Droop Control Strategy. Eigenvalue Analysis is used to determine how the controller droop gains, operating frequency, transmission line parameters, and the operating point affect system stability. The results show that the overall system's dynamic is governed by the sum of the droop gains of the AC/AC converters. Analytical results that give insights on how the different system's parameters affect the critical stability point are presented. The results indicate that system stability is affected by the line's length, the line's R/X ratio, operating frequency and voltage.

eess.SY↗

Angle diversity receiver as a key enabler for reliable ORIS-based Visible Light Communication

Visible Light Communication (VLC) offers a promising solution to satisfy the increasing demand for wireless data. However, link blockages remain a significant challenge. This paper addresses this issue by investigating the combined use of angle diversity receivers (ADRs) and optical reconfigurable intelligent surfaces (ORISs) in multiuser VLC systems. We consider ORIS elements as small movable mirrors. We demonstrate the complementarity of ADR and ORIS in mitigating link blockages, as well as the advantages of using a larger number of ORIS elements due to the increased field-of-view (FoV) at the receiver enabled by the ADR. An optimization algorithm is proposed to maximize the minimum signal-to-noise power ratio (SNR) to deploy a fair communication network. Numerical results show that integrating ADR and ORIS significantly enhances VLC communication performance, achieving an SNR gain of up to 30 dB compared to a system without ORIS, and mitigating communication outages produced by link blockages or out-of-FoV received signals. We also prove that an ADR with a single tier of photodiodes is sufficient to complement ORIS-assisted VLC.

eess.SP↗

D4orm: Multi-Robot Trajectories with Dynamics-aware Diffusion Denoised Deformations

This work presents an optimization method for generating kinodynamically feasible and collision-free multi-robot trajectories that exploits an incremental denoising scheme in diffusion models. Our key insight is that high-quality trajectories can be discovered merely by denoising noisy trajectories sampled from a distribution. This approach has no learning component, relying instead on only two ingredients: a dynamical model of the robots to obtain feasible trajectories via rollout, and a fitness function to guide denoising with Monte Carlo gradient approximation. The proposed framework iteratively optimizes a deformation for the previous trajectory with the current denoising process, allows anytime refinement as time permits, supports different dynamics, and benefits from GPU acceleration. Our evaluations for differential-drive and holonomic teams with up to 16 robots in 2D and 3D worlds show its ability to discover high-quality solutions faster than other black-box optimization methods such as MPPI. In a 2D holonomic case with 16 robots, it is almost twice as fast. As evidence for feasibility, we demonstrate zero-shot deployment of the planned trajectories on eight multirotors. Code and video: https://github.com/proroklab/d4orm

cs.RO↗

Quantum-Based k-Coverage Optimization for UAV-Aided Search and Rescue Missions

In large-scale disaster scenarios, rapid localization of missing persons is a critical challenge for search-and-rescue (SAR) operations. Unmanned aerial vehicles (UAVs) equipped with radio frequency (RF) receivers can support RF-based localization by collecting signals emitted from mobile devices at spatially distributed sensing locations. This paper addresses the resulting waypoint-selection problem: determining a minimum set of UAV waypoints that provides at least threefold coverage of every potential target location. We formulate this task as an extended k-coverage problem that independently defines the UAV-navigable and target regions, and derive an exact-penalty quadratic unconstrained binary optimization (QUBO) formulation with a sufficient penalty condition that preserves feasibility and minimum waypoint cardinality. The QUBO is mapped to an Ising-form cost Hamiltonian and evaluated using the quantum approximate optimization algorithm (QAOA) on both a noise-free simulator and IBM's 127-qubit Eagle processor. On the tested simulator instances, QAOA recovers the known minimum-cardinality solutions. Across the rectangular hardware test cases, the mean 3-coverage ratio exceeded 95%. In the campus-scale evaluation, ten hardware executions achieved 99.3% mean 3-coverage with a 90% feasible-run rate, while the shortest feasible flight path was up to 37.0% shorter than those of the deterministic grid-based baselines. Additional comparisons with classical optimization and learning-based baselines are provided, together with computational and quantum-resource analyses for larger generated instances. These results establish an exact QUBO representation for RF-based SAR waypoint selection and characterize its implementation on current gate-based quantum hardware.

eess.SY↗

Blackbox Small-Signal Modeling of Grid-Connected Inverters in Asymmetrical Power Grids

Power electronic converters are envisaged to be key enablers of modern electric power distribution systems. Grid-connected three-phase inverters are widely used in Smart Grids and microgrids, but also in standard grids. They provide controllability and dynamic decoupling capabilities, which are fundamental in the integration of renewable sources and storage systems and in specialized applications such as FACTS. Nevertheless, power electronics-based systems can exhibit dynamic interactions, which may lead to power quality issues. Although the electrical model of each element of these systems is important for their system-level analysis, they are rarely available. Blackbox modeling strategies are useful for obtaining behavioral models of commercial electronic converters. Most blackbox modeling strategies are focused on dc-dc electronic converters; however, some works have extended these concepts to three-phase converters by means of the dq framework, under the assumption of symmetrical and balanced conditions. This work proposes a new structure in the sequence domain to represent the dynamic behavior of grid-connected commercial converters in asymmetrical conditions. Experimental tests have been performed on a three-phase inverter to identify its blackbox model and its performance has been validated during an asymmetric voltage sag.

eess.SY↗

A Bayesian Adaptive Spectral Surrogate Model for Efficient Probabilistic Optimal Power Flow Evaluation

This paper presents an adaptive stochastic spectral embedding (ASSE) method to solve the probabilistic AC optimal power flow (AC-OPF), a critical aspect of power system operation. The proposed method can efficiently and accurately estimate the probabilistic characteristics (e.g., mean, variance, median, and quantile-based metrics) of AC-OPF solutions while minimizing power losses. Based on estimated AC-OPF decisions (i.e., generator outputs), the confidence interval (CI)-based production cost index can be determined. Specially, an adaptive domain partition strategy is adopted to guide refinement domain selection and partition. The Bayesian compressive sensing-based coefficient calculation algorithm is integrated to enhance its performance. Numerical studies on modified IEEE 9-bus and IEEE 118-bus systems demonstrate that the proposed ASSE method offers accurate and fast evaluations compared to Monte Carlo simulations. Comparisons with a sparse polynomial chaos expansion, Gaussian process regression, and deep neural networks, further illustrate its efficacy in accurately assessing the responses with strongly localized behavior and non-symmetric distributions, providing practical decision-making bounds for generator outputs and operating costs under uncertainty.

eess.SY↗

Situation Awareness for Intelligent Data Distribution in Connected Vehicles

The limitations of on-board sensors and blind spots caused by occlusion cause the reduction of perception quality in autonomous vehicles. In such cases, cooperative perception provides additional data via Vehicle-to-Everything communication to enhance local perception, causing a large volume of data transmission. The vehicle can focus on acquiring and utilizing relevant data according to the prevailing road context by identifying the current traffic situation. To achieve this, we propose a concept for the situation identification of the vehicle using Bird's-Eye-View images. Firstly, the situation around the vehicle is identified using object detection with semantic segmentation, followed by understanding the context of the traffic using a situation identification module consisting of an open-source projective transformation network Cam2BEV and a situation identification neural network. The concept was evaluated and validated by running the software on the CARLA simulator using the in-built RGB camera and the semantic segmentation camera. Additionally, the portability of the situation identification module for real-world applications was verified on Cityscapes and nuScenes urban driving datasets. Overall, the proposed situation identification approach enables efficient sensor data management by prioritizing relevant data to the current traffic situation. The source code is available in the following link: https://github.com/akshaynarla/DySi_Select

cs.RO↗

LMI Properties and Applications in Systems, Stability, and Control Theory

Linear matrix inequalities (LMIs) commonly appear in systems, stability, and control applications. Many analysis and synthesis problems in these areas can be solved as feasibility or optimization problems subject to LMI constraints. Although most well-known LMI properties and manipulation tricks, such as the Schur complement and the congruence transformation, can be found in standard references, many useful LMI properties are scattered throughout the literature. The purpose of this document is to collect and organize properties, tricks, and applications related to LMIs from a number of references together in a single document. In this sense, the document can be thought of as an "LMI encyclopedia" or "LMI cookbook." Proofs of the properties presented in this document are not included when they can be found in the cited references in the interest of brevity. Illustrative examples are included whenever necessary to fully explain a certain property. Multiple equivalent forms of LMIs are often presented to give the reader a choice of which form may be best suited for a particular problem at hand. The equivalency of some of the LMIs in this document may be straightforward to more experienced readers, but the authors believe that some readers may benefit from the presentation of multiple equivalent LMIs.

eess.SY↗

Local Identifiability of Networks with Nonlinear Node Dynamics

We study the identifiability of nonlinear network systems with partial excitation and partial measurement when the network dynamics is linear on the edges and nonlinear on the nodes. We assume that the graph topology and the nonlinear functions at the node level are known, and we aim to identify the weight matrix of the graph. Our main result is that, for almost all static analytic nonlinearities that cross the origin, directed graphs are generically locally identifiable if and only if at least one node is excited in every source component of the condensation graph and at least one node is measured in every sink component. This holds even when all other nodes remain unexcited and unmeasured and stands in sharp contrast to most findings on network identifiability requiring measurement and/or excitation of each node. The result applies to homogeneous feed-forward and recurrent artificial neural networks and generalizes previous literature by considering a broader class of activations and architectures.

math.OC↗

Self-Healing Hybrid Control as a Proxy for Detection and Mitigation of Sensor Attacks in Cooperative Driving

We propose a real-time hybrid controller scheme to detect and mitigate False-Data Injection (FDI) attacks on Cooperative Adaptive Cruise Control (CACC). Our method uses sensor redundancy to create equivalent controller realizations, each driven by distinct sensor subsets but producing identical control inputs when no attack occurs. By comparing control signals and measurements via majority voting, the scheme identifies compromised sensors in real-time and switches to a healthy controller, even under unconstrained attacker switching. The hybrid controller utilizes attack-dependent flow and jump sets, and resets the states of compromised controllers, resulting in a self-healing architecture. Simulation results demonstrate the effectiveness of this approach.

eess.SY↗

Sparse Continuation-Based Eigenvalue Tracking for Power System DDAEs

In this paper, we formulate a continuation method for tracking eigenvalue trajectories in power system models with time-delayed measurement and control signals. Such delays are known to weaken damping and reduce stability margins if not properly accounted for in stability analysis and control design. The proposed formulation follows selected eigenpairs directly from sparse delay differential-algebraic equation (DDAE) models with one or multiple delayed variables, avoiding repeated eigensolutions as system conditions or parameters vary. The continuation parameter can represent system and control parameters, constant delay magnitudes, or parameters governing nonconstant wide-area measurement system (WAMS) delays. The proposed method is validated on a modified IEEE 39-bus system and on a real-world-scale dynamic model of the Irish transmission network, demonstrating accurate tracking of critical eigenvalue trajectories and clear computational advantages over repeated eigensolution-based analysis.

eess.SY↗

Online Learning-Based Control with Guaranteed Error Bounds for a Class of Nonlinear Systems

In this paper, we present a learning-based control for a class of nonlinear systems that guarantees exponential stability as well as bounded output errors. The control is based on the Gaussian Process Submodel Online Learning (GPSOL) algorithm and the Disturbance Error Rate Limiting (DERL) algorithm, both of which were developed in previous work. The GPSOL algorithm provides a method to learn Gaussian Process (GP) models for subsystems online, whereas the DERL algorithm allows to limit the rate of the prediction error of these GP models. The focus of this paper is the utilization of the GP model within an adaptive controller and the derivation of corresponding stability conditions and system peak-to-peak gains by means of linear matrix inequalities (LMIs). These peak-to-peak gains are then used to prescribe a desired prediction error rate for the DERL algorithm to achieve user-defined output error bounds. The gains and the related bounds were successfully verified using a simulation model. Furthermore, results form a successful experimental validation of the bounds and the overall control structure on a pneumatic test rig are presented. While the control scheme and error bounds proposed in this paper are limited to first-order single-input-single-output systems, an extension to certain classes of higher-order and multiple-input-multiple-output systems is expected to be forthcoming.

eess.SY↗

Deterministic Non-Smooth Safety via Dual-Algebraic Control Barrier Functions

This paper presents a dual-algebraic framework for control barrier functions (CBFs) that guarantees deterministic execution using exclusively elementary arithmetic. We develop this deterministic approach to solve a fundamental bottleneck in safety-critical control: pointwise minima compose intersecting safe sets, but generate non-smooth boundaries where standard Lie derivatives fail. Existing mathematical workarounds inject approximation bias, probabilistic non-determinism, or combinatorial execution delays that impede hard real-time hardware certification. By embedding the system state and vector field into the dual-number ring, our method extracts both the composite barrier value and its exact directional derivative in a single evaluation. The standard floating-point minimum deterministically isolates a single vertex of the Clarke generalized gradient for the quadratic-program solver. We prove this selected vertex constitutes a Clarke subgradient and the resulting simultaneous-enforcement safety filter guarantees forward invariance. The arithmetic overhead remains a fixed constant factor, independent of state dimension and constraint count. We extend this framework to finite $\min$/$\max$ Boolean compositions, for which enforcement of the routed constraint of each $δ$-active clause guarantees forward invariance, and to systems of higher relative degree, for which a bivariate truncated-dual evaluation extracts the control coupling without symbolic differentiation. Three numerical examples illustrate the computational scaling.

math.OC↗

On the Convergence of Belief Propagation for Multipath Data Association in Target Tracking

Belief propagation (BP) is widely used for data association (DA) in target tracking. Existing convergence analyses of BP for DA address only the two-way correspondence between targets and measurements, where each target generates at most one measurement per scan. Multipath DA (MPDA) allows a single target to produce multiple measurements via distinct propagation paths, creating a three-way correspondence among targets, paths, and measurements, for which a complete convergence proof has not yet been provided. We provide such a proof for the BP updates in MPDA, establishing convergence to a unique fixed point. Simulations illustrate the convergence behavior of BP in MPDA and demonstrate a favorable accuracy--efficiency trade-off relative to both single-scan and two-scan variants of the multiple-detection multiple-hypothesis tracker.

cs.IT↗

Governing Well in the Algorithmic Age: The Foundations of Digital Statecraft

The digital substrate - data, algorithms, infrastructure, platforms, applications - is being governed without adequate conceptual foundations. The ability and legitimacy required to govern this substrate, and to govern with it, are simultaneously misaligned, contested, and structurally absent. We introduce digital statecraft as the organising concept for this emerging field, arguing that 'digital' reconstitutes the statecraft question rather than merely extending its domain. The concept operates on two dimensions - statecraft over digital systems, concerning the authority and capacity of the state in relation to the digital substrate itself, and statecraft with digital systems, concerning the deployment of algorithmic tools as instruments of governing authority. And it rests on two foundational requirements, technical coherence and legitimate authority, that are genuinely in tension. We derive ten principles of digital statecraft from these foundations, each naming a condition whose absence produces an identifiable and structural governance failure: public interest first, human-machine complementarity, governability by design, systemic coherence, hybrid institutions, adaptive governance, human centricity and civic agency, accountable and traceable authority, judgment across time, and the non-delegable core. This article takes the state as the starting point, the institutional form that developed historically in response to the problem of effective and legitimate public governance, and the only current candidate for which the full set of legitimacy conditions is institutionally available. But the digital statecraft programme holds open a deeper question than just whether states can reform themselves: governing well in the algorithmic age may require rethinking the boundaries, scale, and affiliative basis of statehood itself.

cs.CY↗

A Closed-Loop Thermal Dynamic Model for AI Data Center Cooling Load Simulation

Cooling demand constitutes a significant and flexible component of AI data center electricity consumption, but time-synchronized measurements are scarce and constant coefficient-of-performance models cannot represent thermal dynamics. This letter proposes a closed-loop simulation model which couples a linear thermal dynamic model with deadband-based control to capture the nonlinear cooling dynamics. The model is validated using operational telemetry from the Marconi100 supercomputer. Compared with the baseline, the proposed model reduces the mean absolute error from 95.80 to 20.88~kW and the root-mean-square error from 109.79 to 27.27~kW. Evaluation over approximately 520 daily profiles further shows improved reproduction of daily peak demand and intraday variability. The proposed model provides a computationally tractable means of generating physically interpretable cooling load profiles for power system studies.

eess.SY↗

Design and Analysis of a Programmable Ramp Generator for Frequency Synthesizers

This paper presents a fully digital Programmable Ramp Generator (PRG) for reconfigurable frequency-ramp synthesizers used in Frequency-Modulated Continuous-Wave radar, spread-spectrum clock generation, and test and measurement applications. A novel modular architecture is proposed, integrating a phase-accumulator-based frequency sweep generator and Delta-Sigma Modulator (DSM) that drives a Multi-Modulus Divider (MMD), giving independent digital control over ramp slope, bandwidth, and duration. While prior works demonstrate chirp synthesis, the circuit-level constraints governing reliable PRG operation have not been jointly analyzed. This work identifies and analyzes three such constraints: (a) MMD octave-boundary behavior under dynamic updates, (b) divider-synchronous timing, and (c) DSM limits on step duration and achievable slope. Together, these constraints define the PRG design space, enabling a systematic algorithm that maps application-level specifications directly to hardware parameters. Following this methodology, a 16-bit PRG implemented in TSMC 65 nm CMOS is verified across 18 ramp profiles and hardware prototyping on a Xilinx Spartan-7 FPGA confirms real-time operation.

eess.SY↗

Multi-Domain Graph-Based Modeling of Energy Systems with Applications to Lithium-Ion Batteries

Graph-based models have been shown to provide a structured representation for complex multi-domain energy systems but face limitations when edge power flows depend on non-adjacent states or when a single edge carries multiple power-flow types driven by different inputs. This paper proposes two general extensions to address these limitations: a recursive state-to-input feedback scheme that embeds non-adjacent state dependencies into edge inputs without altering the graph structure, and a parallel edge decomposition method that represents composite interactions using sets of single-input edges while preserving energy conservation at the vertices. The extended framework is demonstrated on a lithium-ion battery module consisting of 36 parallel cells, and the resulting model predicts module temperatures with errors below 1°C. Validation on this electro-thermal battery system demonstrates the effectiveness of the extended framework for multi-domain systems that cannot be represented by previously established graph-based formulations, and indicates its potential for broader application to complex energy systems in control and design studies.

eess.SY↗