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arXiv2026

Extragradient methods with complexity guarantees for hierarchical variational inequalities

In the framework of a real Hilbert space we consider the problem of approaching solutions to a class of hierarchical variational inequality problems, subsuming several other problem classes including certain mathematical programs under equilibrium constraints, constrained min-max problems, hierarchical game problems, optimal control under VI constraints, and simple bilevel optimization problems. For this general problem formulation, we establish rates of convergence in terms of suitably constructed gap functions, measuring feasibility gaps and optimality gaps. We present worst-case iteration complexity results on both levels of the variational problem, as well as weak convergence under a geometric weak sharpness condition on the lower level solution set. Our results match and improve the state of the art in terms of their iteration complexity and the generality of the problem formulation.

math.OC↗

arXiv2026

Uncertainty Estimators for Robust Backup Control Barrier Functions

Designing safe controllers is crucial and notoriously challenging for input-constrained safety-critical control systems. Backup control barrier functions offer an approach for the construction of safe controllers online by considering the flow of the system under a backup controller. However, in the presence of model uncertainties, the flow cannot be accurately computed, making this method insufficient for safety assurance. To tackle this shortcoming, we integrate backup control barrier functions with uncertainty estimators and calculate the flow under a reconstruction of the model uncertainty while refining this estimate over time. We prove that the controllers resulting from the proposed Uncertainty Estimator Backup Control Barrier Function (UE-bCBF) approach guarantee safety, are robust to unknown disturbances, and satisfy input constraints.

eess.SY↗

arXiv2026

Multi-Robot Bearing-based Pose Estimation via Angle Rigidity

This letter proposes a novel distributed pose estimator for multi-robot systems evolving on $\mathrm{SE}(3)$. The robots' positions are estimated in $\mathbb{R}^3$ using angles computed from body-frame bearings, without requiring orientation knowledge. The robots' orientations are then recovered in $\mathrm{SO}(3)$ from the estimated positions, together with bearing and bearing-rate measurements. The estimator accommodates directed sensing topologies and requires only infinitesimal angle rigidity (IAR), thereby relaxing the requirement, common in bearing-based approaches, that every robot acquire at least two bearings. Unlike existing angle-based schemes, the proposed method also estimates the robots' orientations. We prove local uniform exponential stability of the observer, assuming that a subset of robots executes persistently exciting motions. These theoretical results are corroborated through numerical simulations.

cs.RO↗

arXiv2026

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↗

arXiv2026

Electric Vehicle Charging Profile Forecasting Using Hybrid Models

Electric Vehicle (EV) fast charging stations require forecasting techniques both at the single charger level and aggregated level. While for the latter several models exist, forecasting individual EV charging profiles is still underexplored in literature. However, such methods may be potentially used by battery-aware scheduling, leading to a more granular update of the charging station aggregated forecast and provide a more accurate estimation of EVs departure times. Nonetheless, the variable extent of available information in time and in different settings could jeopardize these benefits. For this reason, we propose a hybrid and lightweight method to estimate the EV charging profile before and during the charging process. Besides evaluating this method on multiple EVs from a public dataset, we also assess the impact of different level of information in the time transposition of the charging profile.

eess.SY↗

arXiv2026

Non-Blocking Fault Current Limiting Control of Half-Bridge MMCs for MTDC Transmission

A key protection requirement in multiterminal HVDC (MTDC) transmission systems is to selectively isolate faulted areas using DC circuit breakers (DCCBs) during DC faults while keeping the rest of the systems in operation. However, in MTDC systems using half-bridge modular multilevel converters (HB-MMCs), the lack of inherent fault current suppression capability in HB-MMCs can cause converter blocking before fault clearance by DCCBs. This work presents an active fault current limiting (AFCL) control strategy for HB-MMCs to mitigate DC fault currents, thus avoiding converter blocking and reducing the breaking capability required of DCCBs. In addition, a systematic approach is proposed to design the AFCL control strategy under converter and network stability constraints. The effectiveness of the proposed strategy is demonstrated through PSCAD on two typical MTDC systems during DC faults.

eess.SY↗

arXiv2026

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↗

arXiv2026

A Three-phase and Single-phase Compatible Dual-Mode EV On-Board Charger with Integrated Active Power Decoupling

Onboard charger (OBC) is essential part of Electric Vehicle (EV). High-performance EVs are preferring three phase charging to achieve higher power level. However, the ability to charge from a single phase supply is also required. A dualmode OBC for EVs is proposed, which is capable of operating from both three-phase supply and single-phase supply. The single phase charging comes with the requirement of bulky DC link capacitance due to double frequency current in DC link. The proposed topology eliminates this by achieving Active Power Decoupling (APD) using only one additional relay switch and a small capacitor. The proposed topology is verified under different conditions in a detailed simulation, which shows more than an order of magnitude reduction in DC link capacitance during single phase operation.

eess.SY↗

arXiv2026

Adaptive RIS-aided Communications through ML-based Generation of Phase Masks

Reconfigurable Intelligent Surfaces (RISs) are an attractive technology for Millimeter Wave (mmWave) communications due to their ability to passively reflect incident signals. However, current implementations of RIS rely on performing computationally-intensive algorithms offline to generate phase masks, which are stored as a codebook on the embedded microcontroller on the RIS. The codebook size is restricted by the embedded microcontroller's storage capacity, which limits the ability of the RIS to adapt to evolving channel conditions and deployment scenarios. In this demo, we showcase an Machine Learning (ML)-based solution for dynamically generating new phase masks during runtime. Our approach leverages a ML model deployed on the microcontroller for approximating the output of a phase mask generation algorithm, responding to new inputs while remaining smaller than a codebook.

eess.SY↗

arXiv2026

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↗

arXiv2026

RestoreBench: Can AI Agents Restore Power Flow Convergence?

Large Language Model (LLM) agents increasingly automate multi-step engineering workflows through tool use, interpretation of intermediate results, and iterative planning. Diagnosing and resolving non-convergent power flow cases is a promising yet largely unexplored application, as it requires engineering judgment, experimentation, and decision-making within constrained action spaces. We introduce a benchmark that evaluates these capabilities across multiple LLMs and three architectures: \emph{chatbot}, \emph{single agent}, and \emph{multi-agent} systems. The evaluation covers two power grids and 46 cases per grid, each requiring one or more corrective actions to restore convergence. The benchmark defines the simulation environment, observation and action spaces, and evaluation metrics, providing a reproducible foundation for developing agentic AI systems for power system planning and operation. The code is available at https://github.com/Mansutti081/RestoreBench

cs.AI↗

arXiv2026

Stochastic Nonlinear Model Predictive Control with Gaussian Mixture Uncertainty Propagation

We propose a novel Stochastic Nonlinear Model Predictive Control (SNMPC) framework for nonlinear systems with additive noise. Building on recent advances in nonlinear uncertainty propagation, we show that the state distribution of the system can be tractably approximated over time by Gaussian mixture distributions, with formal error bounds in Wasserstein distance. This representation yields closed-form expressions for expected costs and chance constraints, which become exact for affine constraints and exact up to a constant for quadratic costs. Consequently, the resulting control problem can be solved efficiently via nonlinear programming, while providing formal open-loop guarantees of correctness and asymptotic optimality. Experiments on a set of benchmarks demonstrate that the proposed approach compares favorably with existing methods in nonlinear settings with multi-modal disturbances, where standard approaches lead to poorly scaled solutions and unsafe or overly conservative control actions.

eess.SY↗

arXiv2026

Generalized Semi-Infinite Programming for Robust Optimal Control with Decision-Dependent Uncertainty

Generalized semi-infinite programs (GSIPs) arise in robust optimal control whenever the admissible uncertainty depends on the state or controls. Existing GSIP methods either impose restrictive structural assumptions or require global optimization that scales poorly to control problems. We present a general framework that reformulates any GSIP with mild regularity as an existence-constrained semi-infinite program, smoothing its disjunctive feasibility condition into differentiable existence constraints over a fixed index superset. The resulting program is solved by established adaptive discretization (cutting-plane) methods using only off-the-shelf nonlinear-programming solvers, and converges under standard assumptions. Treating the state trajectory as part of the uncertainty extends the framework to robust nonlinear optimal control with state-dependent uncertainty. We demonstrate it on a nonconvex benchmark GSIP and a satellite de-tumbling problem with dynamically varying inertia.

math.OC↗

arXiv2025

Stability of the Theta Method for Systems with Multiple Time-Delayed Variables

The paper focuses on the numerical stability and accuracy of implicit time-domain integration (TDI) methods when applied for the solution of a power system model impacted by time delays. Such a model is generally formulated as a set of delay differential algebraic equations (DDAEs) in non index-1 Hessenberg form. In particular, the paper shows that numerically stable ordinary differential equation (ODE) methods, such as the trapezoidal and the Theta method, can become unstable when applied to a power system that includes a significant number of delayed variables. Numerical stability is discussed through a scalar test delay differential equation, as well as through a matrix pencil approach that accounts for the DDAEs of any given dynamic power system model. Simulation results are presented in a case study based on the IEEE 39-bus system.

eess.SY↗

arXiv2026

Finite Sample Identification of Analytic Nonlinear Systems

This paper studies the identification of linearly parameterized nonlinear (LPN) systems. Although LPN systems share the same linear parameterization structure as linear systems, they are more challenging to identify. In particular, previous work has shown, through a counterexample based on a piecewise-affine system, that non-active exploration is generally insufficient for LPN system identification. In this paper, we consider LPN systems with real-analytic feature functions. We show that non-active exploration is sufficient for the identification of this class of systems by establishing non-asymptotic convergence rates of least-squares estimation and set-membership estimation. In addition, we provide counterexamples to show that non-active exploration may not be sufficient for system identification for non-real-analytic systems, even if those systems are infinitely differentiable. We present numerical experiments to further support and validate our theoretical results.

eess.SY↗

arXiv2026

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↗

arXiv2026

Physically Constrained Federated Additive Models for O-RAN SLA-Risk Prediction

Proactive service assurance in O-RAN requires predicting per-slice SLA violations before they occur. The prediction model must be auditable by operators and must train across base stations without pooling per-slice KPIs, which are commercially sensitive because slices are leased to individual tenants. Neural additive models (NAMs) offer auditability because each KPI contributes through a visible shape function. However, visibility alone does not guarantee physical validity. On the ColO-RAN testbed dataset, unconstrained NAMs learn effects that contradict wireless physics, for example predicting higher risk when channel quality improves. This failure appears under both local and centralized training, and non-IID federated averaging worsens it. We present Monotone FedNAM, a federated additive model in which KPIs with unambiguous physical direction are represented as monotone splines whose constraints survive FedAvg aggregation by construction, while contestable KPIs remain unconstrained. The model trains and operates as a Non-RT RIC rApp and is compact enough for deployment as a Near-RT RIC xApp. Monotone FedNAM eliminates all monotonicity violations, raises constrained shape consistency from 0.71 to 1.00, generalizes to an unseen scheduling policy, and reduces uplink traffic by 65%, at a cost of 0.04 to 0.07 AUC. These results show that physically constrained federated additive models can support auditable SLA risk inference for multi-tenant O-RAN service assurance

cs.LG↗

arXiv2026

Adaptive Meshing for CPA Lyapunov Function Synthesis

Continuous Piecewise Affine (CPA) Lyapunov function synthesis is one method to perform Lyapunov stability analysis for nonlinear systems. This method first generates a mesh over the region of interest in the system's state space and then solves a linear program (LP), which enforces constraints on each vertex of the mesh, to synthesize a Lyapunov function. Finer meshes broaden the class of Lyapunov function candidates, but CPA function synthesis is more computationally expensive for finer meshes -- particularly so in higher dimensional systems. This paper explores methods to mesh the region of interest more efficiently so that a Lyapunov function can be synthesized using less computational effort. Three methods are explored -- adaptive meshing, meshing using knowledge of the system model, and a combination of the two. Numerical examples for two and three dimensional nonlinear dynamical systems are used to compare the efficacy of the three methods.

eess.SY↗