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 55 records · Page 3Linked to original sources
arXiv2026

Intelligent Fault and Lightning Detection Algorithm for VSC-MTDC grids based on ResNet with Hybrid Attention Mechanism

To address the existing challenges in fault detection for voltage source converter-based multi-terminal DC (VSC-MTDC) grids, this paper proposes an intelligent fault and lightning detection algorithm based on S transform and Residual Network with hybrid attention mechanism (RWHAM).The DC line double-ended initial current traveling waves (ICTWs) are first converted into time-frequency matrices by performing S transform and then visualized as a two-dimensional image. The image effectively characterizes the time-frequency features of ICTWs under different fault and lightning conditions, making it easier to extract critical features for the models. Next, the RWHAM model is constructed and the images are fed into the model to detect internal and external faults and lightning interference. The weight of the key information in the image is increased by hybrid attention mechanism, which in turn improves the fault detection ability of the RWHAM model. Extensive simulations involving 21,620 distinct cases on PSCAD/EMTDC validate the algorithm's high accuracy and rapid response across different types of faults and lightning interference. Furthermore, it exhibits excellent generalization ability when the parameters of VSC-MTDC grids change.

eess.SY↗
arXiv2026

Time-Varying Data as Sheaves: an Invitation to Narratives

Modern science and engineering increasingly rely on time-varying data, yet the mathematical tools used to model temporal phenomena are often developed within separate disciplines, obscuring common principles and limiting the transfer of ideas across fields. This chapter presents the theory of narratives, an abstract framework for time-varying objects of any mathematical kind that supports both theoretical investigations and applications. To illustrate this perspective, the chapter develops three vignettes, each illustrating a different research direction. The first addresses a general concern: What information loss can occur when switching between different representations of temporal data? The second concerns structural and algorithmic approaches: How can we systematically decompose time-varying data into simple pieces and obtain invariants describing its structural complexity? The third is an application to control theory: How can we model multi-agent systems with switching communication topologies? More important than any individual vignette, the central message of this invitation is that a suitable abstract perspective can organize and guide research across remarkably diverse mathematical and scientific domains.

math.CT↗
arXiv2026

Efficient Quantile-Resolved Hosting Capacity Assessment on Nodal Level for Low-Voltage Grids

Hosting capacity - the maximum additional capacity a network can accommodate without violating operational limits - is a key metric in distribution system planning and operation. Decisions on grid reinforcements and the deployment of flexibility management require not only the worst-case HC but also an understanding of the distribution of HC under different likelihoods in load and generation patterns. Quantile-resolved HC distributions provide this view by expressing HC as a function of an acceptable operational limit exceedance likelihood. Monte Carlo sampling is the established approach for computing such distributions but demands large computational resources. Approximations sacrifice either accuracy, the ability to capture uncertainty correlations, or scalability when assessing real-world networks. This paper introduces a computationally efficient method for calculating distributions for quantile-resolved HC. It uses a representation of load samples as multivariate normal distribution, propagated through a linearized power flow model. This allows for leveraging a re-parametrized AC-OPF problem for each hosting capacity quantile. Benchmarking against Monte Carlo-based methods on realistic LV networks demonstrates that the proposed method achieves comparable accuracy with a mean deviation of approx. 3%, while reducing computational time by orders of magnitude. For the exemplary networks the computational time decreases from 11 min to 2 s, and 38 h to 50 s, respectively. The method's scalability is also suitable for recalculation in 15-minute cycles encountered in DSO practice for e.g., real-time grid management.

eess.SY↗
arXiv2026

Online, Reachability-Aware, Sampling-Based Motion Planning

Sampling-Based Model-Predictive Control (MPC) algorithms are a flexible class of controllers used for navigation on a wide range of robotic systems. Historically, such approaches have lacked hard safety guarantees, a shortcoming which we remedy in this work by computing guaranteed reachable-set overapproximations online with a fast, interval-based pipeline. We show that our method achieves similar performance to a state-of-the-art reachability-based planner without the need for the expensive pre-computation step, and can be scaled to systems that are infeasible using existing approaches. Finally, we demonstrate that our technique reduces safety violations by over 99% in a racing simulation and successfully controls a model racecar on real hardware experiments without crashes.

cs.RO↗
arXiv2026

From Learning to Control: Data-Driven Multi-Agent Reinforcement Learning for Multivariable Control in a Microalgae Bioprocess

Effective control of bioprocesses is particularly challenging due to the intrinsic nonlinearity and dynamic variability of living-cell systems. In microalgae-based photobioreactors (PBRs), maintaining stable pH and dissolved oxygen (DO) levels is critical for optimal growth and productivity, yet their strong coupling and sensitivity to environmental fluctuations make multivariable control difficult. This study proposes a novel hybrid offline-online Multi-Agent Reinforcement Learning (MARL) framework for simultaneous pH and DO regulation, leveraging Deep Deterministic Policy Gradient (DDPG) agents to achieve a fully data-driven and model-free control solution. The agents are trained using historical data generated by an expert system, eliminating the need for direct experimentation with the environment. After deployment, the agents operate autonomously, continuously fine-tuning their policies daily to adapt to evolving process dynamics and reject fast transient disturbances. Experimental validation in an open, industrial-scale PBR at the University of Almeria demonstrated the framework's capability to maintain stable operation under realistic conditions. The results confirm that model-free MARL control provides a robust and adaptive alternative for complex bioprocess environments.

eess.SY↗
arXiv2026

AccelMPC: High-Rate, Low-Power FPGA-Accelerated Model Predictive Control for Tiny Drones

Unlocking the potential of tiny aerial robots requires order of magnitude improvements in the performance of embedded edge control. In particular, although recent cached model predictive control (MPC) solvers can handle the fast system dynamics and complex constraints required for agile drone flight, their computational demands remain prohibitive for resource-constrained robots, forcing prior implementations to operate at reduced control rates. AccelMPC overcomes this challenge through an end-to-end co-design approach that jointly optimizes the solver algorithm, numerical representation, hardware mapping, and physical integration. AccelMPC pairs a co-designed FPGA-accelerated alternating direction method of multipliers (ADMM)-based MPC solver with a custom 6g PCB, providing high-bandwidth communication for deployment on a 35g Crazyflie. Hardware experiments demonstrate 1 kHz onboard constrained MPC with dynamic obstacles, up to 15.6x faster solve times and 195.4x improvement in energy-delay product over state-of-the-art embedded microcontroller-based solvers, all while scaling to optimization problems with over 20,000 optimization variables and a comparable number of constraints. We release our PCB design files, firmware, and FPGA solver code open source.

cs.RO↗
arXiv2026

Networked Admissibility-Preserving Control for Directed Safe Coordination

This paper addresses safety-critical coordination for scalar agents whose distributed commands are implemented through constrained physical-input dynamics. Agents communicate over a fixed weighted digraph with a directed spanning tree, while their outputs must remain inside a common moving safety corridor and their realized inputs must satisfy heterogeneous asymmetric bounds. We propose a networked Admissibility-Preserving Control (APC) architecture in which an Admissibility-Preserving Input Realization (APIR) governs physical inputs and a logarithmic barrier coordinate represents the safety corridor. The synthesis yields an exact cascade in which exponentially decaying realization errors drive nonsymmetric consensus dynamics. For every compatible compact initial set, the closed-loop system admits a unique complete solution, renders the moving corridor and actuator intervals forward invariant with uniform margins, keeps commands bounded, and achieves exponential consensus. We derive direction-specific sufficient conditions under which positive and negative control demands remain within their corresponding actuator limits. The analysis yields a closed-form barrier-coordinate limit determined by the left Perron vector and initial APIR mismatch. Under strong connectivity and the stated gain and compatibility conditions, partial pinning propagates a constant barrier reference from a nonempty informed subset and assigns the induced safety corridor trajectory. A non-weight-balanced example illustrates the directional certificate and predicted collective motion.

eess.SY↗
arXiv2026

A Decade of Bayesian Optimization for Controller Tuning and Robot Learning: Tutorial, Review, and Future Prospects

In the past decade, Bayesian optimization (BO) has emerged as a powerful and adaptable framework for automatic controller tuning and robot learning. This article offers a comprehensive overview of the state-of-the-art in BO, designed to support both researchers and practitioners in understanding recent advancements, practical applications, and future research directions. We begin by adopting a practitioner's perspective, illustrating how to effectively set up BO through a representative controller tuning example. We position BO within the broader context of learning paradigms, ranging from deep reinforcement learning to data-driven control, and highlight scenarios where BO is most advantageous. Next, we discuss the diverse range of BO methods that have been developed to tackle complex problems and specific applications. This article provides a unified perspective on the current landscape of BO, emphasizing its relevance to control systems and robotics, and it highlights future prospects by identifying key research challenges and promising avenues for advancing BO in the field. This includes addressing a significant gap in the BO landscape: the lack of standardized benchmark problems specifically for control-related applications. To foster future research and ensure rigorous evaluation, we start an effort towards a lightweight benchmark suite for control engineering and robotics. We also present metrics and best practices to facilitate direct comparisons between new BO algorithms and established state-of-the-art methods.

cs.RO↗
arXiv2026

Fast Constraint Extraction for Corrective Control under STL Specifications via Logical Dependency Tracking

Ensuring the satisfaction of Signal Temporal Logic (STL) specifications under uncertainty is challenging, as reachability-based monitoring provides guarantees but does not indicate how to restore satisfaction when it becomes indeterminate. A key difficulty is identifying which uncertain components actually affect global satisfaction, especially for nested formulas. This paper introduces a logical dependency tracking framework that propagates uncertainty through the STL structure and captures the causal contribution of reachable sets to satisfaction. By associating markers to uncertain predicates and propagating them via three-valued semantics, we extract in milliseconds a compact Disjunctive Normal Form (DNF) of sufficient constraints, avoiding combinatorial enumeration. As an application, we formulate control correction as a minimum-effort optimization problem. Using zonotopic reachability, the derived constraints are enforced via linear programming, yielding corrections that guarantee STL satisfaction under bounded uncertainty and provide certified probabilistic bounds in the stochastic case. We demonstrate the approach on a nonlinear system with nested STL specifications, showing that dependency tracking enables efficient and formally guaranteed correction. The tracking implementation is available at https://github.com/Antoine-Bst/STL-Three-Valued-Clause-Filtering/.

cs.LO↗
arXiv2026

Data-driven design of steady-state feedforward inputs for nonlinear systems under partial measurement

Designing trajectory tracking controllers for nonlinear systems remains a significant challenge, traditionally requiring precise mathematical models and complex analytical derivations. While the Internal Model Principle (IMP) provides a robust theoretical foundation for such problems, its application is often hindered by model uncertainty and the inherent complexity of nonlinear controller synthesis. This work proposes a practical data-driven control framework that bypasses the need for an explicit first-principles model by utilizing raw input-output data. By integrating fundamental results from IMP theory with nonlinear system analysis, the proposed approach improves design tractability. The framework's efficacy is validated through numerical simulations on two distinct nonlinear platforms: a mechanical load with a nonlinear friction term and an electrohydraulic actuator system.

eess.SY↗
arXiv2026

Distributed Physical Layer Authentication and Collaborative RSMA in Non-Terrestrial Networks via Graph Reinforcement Learning

Existing physical-layer authentication (PLA) schemes for non-terrestrial networks (NTNs) often rely on single-anchor verification, lack joint authentication-transmission design, and ignore tag privacy leakage under eavesdropping. In this paper, we consider passive, location-aware, static eavesdroppers without access to legitimate channel state information (CSI). Under this threat model, we propose secure adaptive federated authentication for multi-zone NTN systems (SAFA-MZ) that maximizes secrecy spectral efficiency (SSE) while ensuring authentication reliability, power limits, and coverage constraints. The main idea is to embed group-level authentication tags into a collaborative multi-layer rate-splitting multiple access (RSMA) transmission structure. Private and common signals are jointly beamformed, artificial noise (AN) is used to reduce information leakage, and group differential privacy (GDP) protects tag information against inference attacks. In addition, users are grouped by semantic priority to allocate SSE based on information importance. We formulate a joint SSE maximization problem under authentication reliability and probabilistic secrecy constraints, optimizing high-altitude platform station (HAPS) placement, user association, and RSMA power allocation. The resulting problem is solved using a repair-based cross-entropy method (RCEM) and a graph-aware advantage actor-critic algorithm (GA2C). RCEM scales quadratically with the number of users, while GA2C scales linearly and achieves scalable, low-latency inference. Simulation results under both colluding and non-colluding eavesdroppers show that the proposed method improves average SSE by up to 135% over single-connect transmission and 21% over the scheme without AN. These results confirm SAFA-MZ offers a scalable and secure solution for dynamic NTN environments.

eess.SP↗
arXiv2026

Strategic Dispatching Equilibrium under Competition in Multi-Regional Ride-Hailing Markets

This paper studies competition between ride-hailing companies in a multi-regional market through a static non-cooperative game, in which each company allocates its fleet across regions to maximize profit. Under the assumptions of the proposed framework, we establish an equilibrium existence result on a restricted feasible set by analyzing the asymptotic quasi-concavity of the payoff functions. A numerical study of a two-company, two-region market shows that relative fleet size strongly affects equilibrium dispatching strategies: the smaller company tends to concentrate its fleet in one region and serves both regions only after its fleet share (in percentage) exceeds a threshold. In the numerical setting considered here and under the Logit demand specification, more balanced fleet sizes are associated with higher total industry profit and higher total served demand than strongly asymmetric fleet configurations.

eess.SY↗
arXiv2026

Robust Adaptive Discrete-Time Control Barrier Certificate

This work develops a robust adaptive control strategy for discrete-time systems using Control Barrier Functions (CBFs) to ensure safety under parametric model uncertainty and disturbances. A key contribution of this work is establishing a barrier function certificate in discrete time for general online parameter estimation algorithms. This barrier function certificate guarantees positive invariance of the safe set despite disturbances and parametric uncertainty without access to the true system parameters. In addition, real-time implementation and inherent robustness guarantees are provided. The proposed robust adaptive safe control framework demonstrates that the parameter estimation module can be designed separately from the CBF-based safety filter, simplifying the development of safe adaptive controllers for discrete-time systems. The resulting safe control approach guarantees that the system remains within the safe set while the controller adapts to model uncertainties, making it a promising strategy for discrete-time safety-critical systems.

eess.SY↗
arXiv2026

Experimental Validation of Combined Imaging and Vibration Mitigation for High-Altitude Platforms

The demand for continuous, high-resolution aerial monitoring is driving interest in High- Altitude Pseudo-Satellites (HAPS), which offer a cost-effective alternative to satellites with greater flexibility. While HAPS platforms provide valuable Earth observation potential, se- lecting an optical sensor requires balancing resolution and payload constraints, given their lightweight structures. We propose a multidisciplinary concept to enhance the Earth obser- vation capabilities of HAPS using a commercially available, weight-efficient distributed camera network, combined with advanced image processing and active vibration control. A super- resolution pipeline is introduced, where images from individual cameras are preprocessed and fed into a super-resolution algorithm. The low structural rigidity of HAPS platforms increases their sensitivity to low-frequency vibrations, which must be mitigated through active control to preserve imaging performance. A nominal H-infinity mixed-sensitivity controller is designed to suppress resonance peaks at camera locations. The methodology is experimentally validated on a simplified wing platform designed for real-time testing. Quantitative evaluation based on nine metrics demonstrates significant image quality improvements, highlighting the effectiveness of H-infinity control in mitigating low-frequency elastic modes and stabilizing the platform for super- resolution. These results show that lightweight commercial sensors, combined with advanced image processing and control, can deliver high-quality imaging for future HAPS remote sensing missions.

eess.SY↗
arXiv2026

Safe-by-Design: Approximate Nonlinear Model Predictive Control with Real Time Feasibility

This paper proposes a computationally lightweight, continuous-time receding-horizon nonlinear model predictive control (MPC) approach with infinitesimal prediction horizon. Unlike discrete-time one-step MPC schemes, this methodology permits the formulation of small-sized convex quadratic pro- grams (QPs) for feedback yet recovers the theoretical guarantees of quasi-infinite horizon MPC. The convex QP comes with a small computational footprint, which is advantageous for real- time application where runtime guarantees must be given. We demonstrate the effectiveness of the proposed approach when compared to other constrained control techniques through numerical experiments for nonlinear constrained spacecraft control.

math.OC↗
arXiv2026

Maximum-Entropy Random Walks on Hypergraphs

Random walks are fundamental tools for analyzing complex networked systems, including social networks, biological systems, and communication infrastructures. While classical random walks focus on pairwise interactions, many real-world systems exhibit higher-order interactions naturally modeled by hypergraphs. Existing random walk models on hypergraphs often focus on undirected structures or do not incorporate entropy-based inference, limiting their ability to capture directional flows, uncertainty, or information diffusion in complex systems. In this article, we develop a maximum-entropy random walk framework on directed hypergraphs with two interaction mechanisms: broadcasting where a pivot node activates multiple receiver nodes and merging where multiple pivot nodes jointly influence a receiver node. We infer a transition kernel via a Kullback--Leibler divergence projection onto constraints enforcing stochasticity and stationarity. The resulting optimality conditions yield a multiplicative scaling form, implemented using Sinkhorn--Schrödinger-type iterations with tensor contractions. We further analyze ergodicity, including projected linear kernels for broadcasting and tensor spectral criteria for polynomial dynamics in merging. The effectiveness of our framework is demonstrated with both synthetic and real-world examples.

eess.SY↗
arXiv2026

Bilevel MPC for Linear Systems: A Tractable Reduction and Continuous Connection to Hierarchical MPC

Model predictive control (MPC) has been widely used in many fields, often in hierarchical architectures that combine controllers and decision-making layers at different levels. However, when such architectures are cast as bilevel optimization problems, standard KKT-based reformulations often introduce nonconvex and potentially nonsmooth structures that are undesirable for real-time verifiable control. In this paper, we study a bilevel MPC architecture composed of (i) an upper layer that selects the reference sequence and (ii) a lower-level linear MPC that tracks such reference sequence. We propose a smooth single-level reduction that does not degrade performance under a verifiable block-matrix nonsingularity condition. In addition, when the problem is convex, its solution is unique and equivalent to a corresponding centralized MPC, enabling the inheritance of closed-loop properties. We further show that bilevel MPC is a natural extension of standard hierarchical MPC, and introduce an interpolation framework that continuously connects the two via move-blocking. This framework reveals optimal-value ordering among the resulting formulations and provides inexpensive a posteriori degradation certificates, thereby enabling a principled performance-computational efficiency trade-off.

eess.SY↗
arXiv2026

Equivalence Between Continuous-Time Risk-Sensitive Control and Rényi Divergence Minimization

In this study, we show that a continuous-time risk-sensitive control problem is equivalent to a Rényi divergence minimization problem over trajectory path measures. Reformulating stochastic optimal control as probabilistic inference via Kullback-Leibler (KL) divergence minimization avoids the computational intractability of the Hamilton-Jacobi-Bellman equation. However, standard KL control is inherently risk-neutral, and recent minimax extensions remain restricted to risk-averse settings. Our equivalence result resolves this limitation by offering a unified probabilistic framework for arbitrary risk attitudes in continuous-time nonlinear systems. Based on Girsanov theorem, we explicitly map the risk sensitivity to the Rényi divergence order, deriving a noise-dependent control penalty scaled by risk preference. This formulation seamlessly modulates tail-weighting behaviors, interpolating between zero-forcing for risk-averse policies and mass-covering for risk-seeking policies. These findings bridge stochastic control and information-theoretic inference, providing a foundation for sampling-based control algorithms.

math.OC↗