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cs.SY: explore 114 source-linked works published from 2024 to 2026, with original documents and citations.

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Sources: arxiv. Collection updated 2026-09-14. Counts describe this index, not the complete source archives.

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

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

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

Enhancing Interpretability of Stochastic Programming Solutions: A Multiparametric Approach

Stochastic programming (SP) is a powerful framework for decision-making under uncertainty, but its practical adoption in industry is often hindered by the difficulty in understanding the causal relationships that drive optimal solutions. In the two-stage SP, strategic first-stage decisions are coupled with operational second-stage recourse decisions. When the number of scenarios under consideration is large, understanding the direct link between the uncertainty realization and optimal recourse strategy becomes computationally and cognitively demanding. Common approaches to improve interpretability include trained classification trees or scenario reduction, replacing the large scenario set with a representative subset. This is often achieved through post-hoc clustering (e.g., k-means) based on uncertainty realizations or optimal recourse decisions. While useful, these methods only provide a statistical approximation of the solution space and may fail to reveal the underlying structural properties of the recourse problem that drive optimal first-stage decisions. This work introduces a novel, deterministic approach to explainability using multiparametric programming (mp) within a Benders decomposition framework. We reformulate the recourse subproblem as a multiparametric linear program, generating an explicit map of Critical Regions (CRs), which are polyhedral partitions of the uncertainty space. This allows us to cluster scenarios analytically rather than statistically. We demonstrate this methodology on a supply chain planning under demand uncertainty. Our results show that 100 stochastic scenarios map to exactly six critical region clusters. This mapping allows us to explain optimal capacity planning decisions as a precise trade-off between specific operational modes, providing a fully transparent interpretation of the stochastic solution.

math.OC

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

Robust Semi-passive Velocity Field Control with Boundedness Guarantees for Safe Interaction between Mechanical Systems and Physical Environment

Controllers that guarantee energetic passivity with respect to the pair of external force and velocity realize safe interaction between the mechanical system and its physical environment. However, solely adhering to energetic passivity constraints may impose fundamental limitations on control performance and, in some cases, prevent the successful execution of controlled tasks. In addition, external disturbances from the physical environment can drive the system energy level and states beyond operational regions, thereby undermining task performance and safety. In this paper, we study a robust time-varying semi-passive velocity field control to aim to relax the inherently conservative nature of fully passive control methods in a controlled manner. Specifically, the proposed control method guarantees passivity of the closed-loop system with respect to the force-velocity input-output pair when the energy level exceeds a predefined level, while permitting non-passive behaviors to preserve task performance otherwise. Furthermore, the energy level and the states of the closed-loop system are proved to converge to bounded domains even in the presence of unpredicted disturbances. Additionally, the proposed method also enables constraining power flow between the closed-loop system and its physical environment to enhance safety in the interaction process. Numerical simulation examples demonstrate the effectiveness of the proposed method.

eess.SY

A High-Resolution Synthetic EV Charging Dataset for Cold-Climate Distribution Grid Impact Analysis: Trondheim, Norway (2020-2030)

This data article presents a high-resolution, long-term synthetic electric-vehicle (EV) charging dataset for Trondheim, Norway, spanning February 2020 to December 2030. Empirically grounded in 14 months of historical charging logs from December 2018 to January 2020, the dataset captures session-level behavioral patterns, including delivered energy, plug-in duration, connection schedules, user categorization (private vs. shared), seasonal variations, public-holiday effects, and daily ambient temperature dependencies. To model future electrification dynamics, the synthetic generation pipeline integrates historical session records, calendar and weather features from MET Norway, annual EV-adoption growth multipliers derived from Statistics Norway (SSB) registration trajectories, a daily session-count model, a Conditional Tabular Generative Adversarial Network (CTGAN), seasonal Kernel Density Estimation (KDE), and post-generation physical charger-power feasibility correction. Under a standardized 7.2 kW AC charging constraint, the resulting medium EV-adoption scenario dataset contains 76,993 hourly charging-activity records. The hourly profile is activity-based rather than a complete continuous hourly time series; hours with no allocated EV charging energy are not included. The records provide total hourly charging energy, equivalent average charging power, active session counts, private/shared user load breakdowns, ambient temperature features, and calendar indicators. The dataset provides a validated cold-climate benchmark for distribution-grid impact assessment, transformer-loading analysis, EV charging-demand forecasting, charger-capacity planning, energy-management optimization, and the development of data-driven smart-charging control strategies.

eess.SY

Control of Decommissioned Satellites and Space Debris Using CubeSats with Ion Electrospray Engines

The emergence of the New Space era has led to a rapidly increasing number of satellites in Low Earth Orbit (LEO). Consequently, more stringent deorbiting requirements have recently been imposed to avoid the Kessler syndrome in LEO. This has resulted in the proposal of new concepts for space debris removal, including attaching CubeSats to space debris as a promising mitigation strategy. The initial phase of this strategy involves stabilizing and controlling the debris' attitude. This paper proposes an attitude control design for decommissioned satellites using attached CubeSats with staged ion electrospray engines (iESE). The compact design of iESE combined with staging provides increased reliability and mission durations. The large uncertainty in the dynamics of the combined system, i.e. satellite and attached CubeSats, poses a significant challenge to the control design. The approach taken here uses the robust control framework, specifically $μ$-synthesis, to tackle this challenge. The feasibility of the approach is demonstrated on a decommissioned satellite with multiple flexile appendages.

eess.SY

Subspace Based Identification of Errors-in-Variables Linear Descriptor Systems

The identification of linear descriptor systems (DAEs) from noise-corrupted data makes two critical assumptions: requirement of an \textit{a priori} classification of variables into inputs and outputs, and a pre-specified structural assumption with respect to the index of the system. This paper proposes a data-driven methodology for identifying index-0 and index-1 DAEs within an errors-in-variables framework. We extend a subspace-based iterative PCA (SMI-IPCA) approach to the behavioral setting, treating all measured variables as a unified augmented vector to avoid classification bias. This method enables systematic estimation of the noise variances, the number of algebraic and differential output variables, while simultaneously identifying the algebraic constraints and kernel representation of the dynamic system corresponding to its minimal realization order without prior structural knowledge. Simulation studies on index-0 and index-1 systems demonstrate the effectiveness of the proposed approach and its practical applicability.

eess.SY

Converse Barrier Certificates for Set-Based Stochastic Reach-Avoid Verification

Recent work established sufficient and necessary barrier-like conditions for infinite-horizon reach-avoid verification of stochastic discrete-time systems from a single initial state. Whether such a converse characterization extends to a set of initial states, however, remains open. In this paper, we answer this question affirmatively for compact initial sets. We consider a uniform reach-avoid specification requiring the reach-avoid probability to exceed a prescribed threshold for every initial state in a compact set. Under appropriate assumptions, including continuous system transitions, together with a strict uniform probability margin, we extend the pointwise converse characterization to the uniform setting.

eess.SY

A Roadmap to Available ICS Datasets and Testbeds for Cybersecurity Research

Industrial Control Systems (ICS) are the backbone of many critical infrastructure sectors; however, their growing level of connectivity, long lifespan and integration with the Information Technology (IT) environment introduces numerous cybersecurity challenges. The merging of Operational Technology (OT) and IT along with the deployment of Industry 4.0 technologies increases the attack surface of ICS environments, which in turn makes them more vulnerable to advanced cyber threats. Therefore, many researchers have shown interest in the field of cybersecurity of ICS. The topics of intrusion detection, anomaly detection, threat intelligence, attack simulation and resilience assessment of ICS have received much attention. Nevertheless, the development and testing of cybersecurity solutions for ICS remains to be challenging due to the lack of appropriate datasets and experimental environment. The main objective of this paper is to provide the roadmap of existing ICS cybersecurity datasets, testbeds and digital twins. This paper presents various taxonomies along with systematic analysis of architecture, characteristics, capabilities, pros and cons of these tools. The results of the analysis demonstrate the presence of persistent problems such as lack of standardized benchmarking datasets, lack of modern attack scenarios, insufficient number of datasets based on real operational traffic and difficulty in validating artificial intelligence-driven cybersecurity solutions. In addition to summarizing current research on ICS cybersecurity datasets and testbeds, this roadmap provides the identification of research gaps and recommendations on creation of new tools.

cs.CR

Neural 3D Object Reconstruction with Small-Scale Unmanned Aerial Vehicles

Miniaturized Uncrewed Aerial Vehicles (UAVs) can access indoor and hard-to-reach spaces, but severe constraints on payload and autonomy have limited their use in demanding tasks such as high-quality 3D reconstruction. We introduce a novel system architecture that enables autonomous, high-fidelity 3D scanning of static objects with sub-100 gram UAVs. Our core innovation lies in a closed-loop active viewpoint selection framework specifically tailored for ultra-constrained micro-platforms, advancing beyond standard static or offline active reconstruction methods. The framework establishes a dual-reconstruction pipeline that creates a real-time (RT) feedback loop between data capture and flight control. A near-RT process uses Structure-from-Motion (SfM) to generate an instantaneous point-cloud of the object. A systematic trajectory adaptation algorithm analyzes the model quality on the fly and dynamically adapts the UAV's trajectory based on parameterized spatial partitioning to intelligently capture new images of poorly covered areas, ensuring comprehensive acquisition. For the final, high-fidelity output, a non-RT pipeline employs a Neural Radiance Fields (NeRF)-based Neural 3D Reconstruction (N3DR) approach, fusing SfM-derived camera poses with precise external location data, evaluated across both radio-based Ultra Wideband (UWB) and visual motion-capture setups, to correct sensor noise and achieve superior accuracy. We implemented and validated this architecture using Crazyflie 2.1 UAVs. Our experiments, conducted in both single- and multi-UAV configurations show that algorithmic dynamic trajectory adaptation consistently improves reconstruction quality over static flight paths. This work demonstrates a scalable and autonomous solution that unlocks the potential of miniaturized UAVs for fine-grained 3D reconstruction, a capability previously reserved for much larger platforms.

cs.RO

Spatiotemporal Continual Federated Learning for Agentic Multi-UAV Edge Networks: Mitigating Catastrophic Forgetting

This paper addresses multi-objective conflicts and catastrophic forgetting in uncrewed aerial vehicle (UAV) networks across dynamic spatiotemporal environments. Conventional multi-agent reinforcement learning (MARL) algorithms suffer from severe policy degradation during sequential task transitions. We propose a spatiotemporal continual federated learning (SCFL) framework driven by the group-decoupled multi-agent proximal policy optimization (G-MAPPO) algorithm. SCFL incorporates a three-stage geometric alignment mechanism: it resolves local gradient conflicts via group-decoupled policy optimization (GDPO), mitigates spatial Non-Independent and Identically Distributed (non-IID) client drift through adaptive cosine aggregation, and suppresses inter-task interference via global temporal orthogonal projection without raw experience replay. Evaluations show that SCFL achieves superior robustness over federated baselines, maintaining spatial service reliability above 0.95 and a load balancing index of approximately 0.95 during non-stationary transitions. A longitudinal self-degradation analysis further shows that SCFL preserves historical knowledge with near-zero performance variation in spatial reliability and QoS under moderate loads from 40 to 120 users, while revealing its operating boundary under extreme congestion with 140 users due to hard projection constraints. The framework provides a scalable, communication-efficient approach for autonomous aerial network orchestration.

cs.NI

Performance Guarantees for Data-Driven Sequential Decision-Making

The solutions to many sequential decision-making problems are characterized by dynamic programming and Bellman's principle of optimality. However, due to the inherent complexity of solving Bellman's equation exactly, there has been significant interest in developing various approximate dynamic programming (ADP) schemes to obtain near-optimal solutions. A fundamental question that arises is: how close are the objective values produced by ADP schemes relative to the true optimal objective values? In this paper, we develop a general framework that provides performance guarantees for ADP schemes in the form of ratio bounds. Specifically, we show that the objective value under an ADP scheme is at least a computable fraction of the optimal value. We further demonstrate the applicability of our theoretical framework through several applications: data-driven robot path planning, pendulum stabilization, and multi-agent sensor coverage.

eess.SY

Emission reduction potential of freeway stop-and-go wave smoothing

The real-world potential of stop-and-go wave smoothing at scale remains largely unquantified. Smoothing freeway waves requires opening a gap large enough for them to dissipate, but that gap is often impractically large. We propose a counterfactual wave-smoothing benchmark that reconstructs a smooth, feasible trajectory from each empirical trajectory by solving a quadratic program with fixed boundary conditions and a maximum-gap constraint, and we use the MOVES model to estimate the resulting emission reduction potential. Applying the framework to nine weeks of weekday peak-period data from the I-24 MOTION testbed, which exhibits rich day-to-day variation in wave dynamics, we find meaningful potential for passenger cars under a 0.1-mile maximum-gap constraint: average CO2 reductions of 9.80% to 14.08% across lanes, with 12.94% to 25.70% in CO, 24.29% to 29.76% in HC, and 29.40% to 36.03% in NOx. Trucks emerge as particularly attractive targets, showing roughly twice the reduction potential of passenger cars.

eess.SY

Temporal Property-driven Design Space Exploration with Reinforcement Learning for Cyber-Physical Systems

Design-space exploration of configurable Cyber-Physical Systems (CPS) requires executable evaluation when design choices affect timing, fault propagation, recovery behavior, and temporal-property satisfaction. Repeated stochastic executions make exhaustive exploration impractical for large design spaces. This paper presents a temporal-property-driven CPS design workflow using Reinforcement Learning (RL). At design time, the RL agent selects subsystem alternatives to assemble a candidate system model. The model is then evaluated through simulation, during which online temporal-property monitors observe runtime traces and produce functional-property violation indicators. These indicators are combined with evaluated non-functional terms for budget, recoverability, sustained compliance, and operational use to calculate the reward used for subsequent candidate selection. The workflow is evaluated on a methane-sensitive mine-pump CPS. The corresponding executable case-study model is provided as additional contribution. RL-guided search identifies the highest-reward design observed in the experiments after 26 episodes (corresponds to 130 executable simulations). These designs were reached with fewer simulations than surrogate-guided Bayesian Optimization and population-based Genetic Algorithm baselines under the same executable model and reward formulation. Ablation study results indicate that value-based feedback and reuse of previous simulation traces contribute to this reduction.

eess.SY

Relative-Degree Wall Restricts Passivity-Based Stability Analysis in Inverter-Dominant Grids

This letter reveals a fundamental limitation of passivity-based distributed stability analysis in power systems. Under the standard formulation, passivity certification inherently imposes a relative-degree compatibility constraint that excludes many high-fidelity inverter dynamic models (e.g., those that include electromagnetic transients). Potential extensions of passivity frameworks are discussed to break this limitation.

eess.SY

Proof of a Conjecture of De Cock and De Moor

De Cock and De Moor proposed a conjecture connecting two seemingly different viewpoints in stochastic subspace identification, one based on Lyapunov equations and the other on principal angles and canonical correlations. The conjecture was recorded as Problem 9.1 of \emph{Unsolved Problems in Mathematical Systems and Control Theory}. We give a direct finite-dimensional proof under the natural nonresonance condition, without requiring stability or diagonalizability. The key mechanism is the rank-one perturbation, which exposes a hidden Cauchy-matrix structure and reduces the problem to rational interpolation. A density and continuity argument then removes the generic spectral assumptions. The result strengthens the original statement. The eigenvalues agree with algebraic multiplicity, a nonsingularity assumption of the original formulation becomes automatic, and on a dense open set of parameters the two matrices are similar rather than merely cospectral. While this manuscript was being prepared, Gillberg and Löfberg independently posted a proof based on a Lyapunov-kernel identity and the classical $AB$--$BA$ principle. The proof given here was developed independently and follows a different route.

math.OC
Compare source metadata on this page
WorkPublishedSource identifierSource
LMI Properties and Applications in Systems, Stability, and Control Theory2026-08-311903.08599arxiv
Online Learning-Based Control with Guaranteed Error Bounds for a Class of Nonlinear Systems2026-08-312605.10152arxiv
On the Convergence of Belief Propagation for Multipath Data Association in Target Tracking2026-08-312607.08521arxiv
Enhancing Interpretability of Stochastic Programming Solutions: A Multiparametric Approach2026-08-312608.30137arxiv
Multi-Domain Graph-Based Modeling of Energy Systems with Applications to Lithium-Ion Batteries2026-08-312608.30157arxiv
Robust Semi-passive Velocity Field Control with Boundedness Guarantees for Safe Interaction between Mechanical Systems and Physical Environment2026-08-312608.30193arxiv
A High-Resolution Synthetic EV Charging Dataset for Cold-Climate Distribution Grid Impact Analysis: Trondheim, Norway (2020-2030)2026-08-312608.30199arxiv
Control of Decommissioned Satellites and Space Debris Using CubeSats with Ion Electrospray Engines2026-08-312608.30215arxiv
Subspace Based Identification of Errors-in-Variables Linear Descriptor Systems2026-08-312608.30259arxiv
Converse Barrier Certificates for Set-Based Stochastic Reach-Avoid Verification2026-08-312608.30318arxiv
A Roadmap to Available ICS Datasets and Testbeds for Cybersecurity Research2026-08-312608.30332arxiv
Neural 3D Object Reconstruction with Small-Scale Unmanned Aerial Vehicles2026-08-302509.12458arxiv
Spatiotemporal Continual Federated Learning for Agentic Multi-UAV Edge Networks: Mitigating Catastrophic Forgetting2026-08-302601.21861arxiv
Performance Guarantees for Data-Driven Sequential Decision-Making2026-08-302603.20553arxiv
Emission reduction potential of freeway stop-and-go wave smoothing2026-08-302603.21476arxiv
Temporal Property-driven Design Space Exploration with Reinforcement Learning for Cyber-Physical Systems2026-08-302608.23440arxiv
Relative-Degree Wall Restricts Passivity-Based Stability Analysis in Inverter-Dominant Grids2026-08-302608.29474arxiv
Proof of a Conjecture of De Cock and De Moor2026-08-302608.29479arxiv

These are bibliographic comparisons, not experimental rankings. Follow the original document for methods and conditions.