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新论文 · v1arXiv:2610.39378

Limit Theorems and Optimal Policies for Flock Flight through Poisson Forests

Lav R. Varshney

原文摘要

Inspired by birds flying through cluttered environments, Karaman and Frazzoli showed that high-speed flight through an ergodic random forest exhibits an ultimate speed limit: above a critical speed, almost surely no infinite collision-free trajectory exists for a single bird. This paper formulates a corresponding bulk-transport problem for many birds. We construct an explicit directed corridor network in a Poisson disk forest and impose a common temporal headway at its waypoints. The maximum current equals a minimum vertex-cut capacity and is achieved by repeated flight along vertex-disjoint paths. The same schedule maximizes completed traversals at every finite horizon, with constant forward speed and no internal waiting. For a forest belt of fixed depth, exact transverse superadditivity yields an almost-sure deterministic current-density limit. A uniform shape theorem for an asymmetric planar dual identifies the current when depth and width grow together. For diverging depth bounded by a constant times width, this current equals the width-first, depth-second limit and is independent of the positive aspect ratio. When diagonal corridors are available, bulk current is positive at sufficiently low obstacle intensity and zero at sufficiently high intensity. For stochastic demand, an upstream admission queue is stable at every rate below the finite-network capacity.

eess.SY · cs.RO · math.PR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39320

BEVLight: Generalizable Traffic Signal Control from UAV Bird's-Eye Views

Maonan Wang, Yuxin Cai, Junming Liu, Zhengyan Huang, Yi Yu, Ding Wang, Yifeng Zhang, Yirong Chen

原文摘要

Learning-based traffic signal control (TSC) has achieved promising performance, but most methods rely on structured traffic states that must be estimated from sensor observations in real deployments. Visual TSC learns signal decisions directly from images, yet existing methods depend on fixed roadside cameras and remain tied to specific intersection configurations and visual domains. UAVs offer on-demand bird's-eye-view (BEV) observations that jointly capture intersection topology and traffic dynamics, making them a promising sensing platform for visual TSC. We present BEVLight, a generalizable TSC framework that directly maps BEV observations to signal actions. BEVLight grounds visual features onto individual lanes and composes them along the junction topology into movement and phase representations, allowing a shared policy to score candidate phases across heterogeneous intersection layouts and signal plans. To train the policy, we develop a web-based virtual teleoperation platform and collect over 5K human control decisions together with their underlying traffic states. Simulator-provided traffic states first supervise lane- and movement-level representations, after which human phase selections and rankings supervise the phase scorer. Re-rendering the recorded trajectories under diverse appearances preserves both forms of supervision and encourages robustness to visual changes. Closed-loop experiments demonstrate zero-shot generalization across traffic demands, signal plans, and intersections: BEVLight reduces average waiting time by 7--9\% relative to perception-to-control visual baselines and matches or outperforms structured-state RL controllers that receive ground-truth traffic states, achieving the lowest waiting time in 12 of 16 scenarios. An offline study further shows that BEVLight transfers to real UAV observations without target-domain adaptation.

eess.SY · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39155

REALM: Identifying Minimal Task Assets for LLM Agents

Zihou Ren, Byungchul Tak, Long Wang

原文摘要

Large language model (LLM) agents often receive access to more files, accounts, and services than a task requires. Prompt injection turns this excess access into an opportunity to redirect execution toward unrelated resources. Applying least privilege therefore requires identifying the assets needed by a task, including assets whose identities emerge only during execution. We present REALM (Required Execution Assets Linked to a Mission), an approach to identifying and maintaining a task's required assets and their permitted uses. The approach represents required assets as Dependency Graph nodes, resolves prerequisite relationships, and admits newly discovered assets through source-scoped selection. It separates discovering an asset from authorizing its use, while preserving the agent's freedom to choose its computation. The REALM Builder maintains the selected grants and projects Asset Files; the Partial API Manager exposes resource-bound Partial APIs to the Agent inside a Task Container. Across 60 clean tasks and 560 injected cases in AgentDyn, it completes 40.0% and 36.4% of tasks and decreases ASR from 16.6% to 0.2% with qwen3.5-flash as the base model.

cs.CR · cs.AI · eess.SY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39152

Unlocking with the Heart: Investigating User Perceptions of ECG-Based Biometric Authentication

Farnaz Farid, Linet Momposhi, Tamara Watson, Alana Maurushat, Abubakar Bello, Farhad Ahamed

原文摘要

Password-based authentication remains challenging for many users, especially for those who face usability and accessibility issues. The burden of maintaining security can cause user fatigue, frustration and non-engagement. To address these issues, we conducted a pilot study to assess ECG-based biometric authentication as a passwordless alternative and to gather adult participants' perspectives on this approach. The research consisted of a survey study, an authentication prototype and a follow-up usability experience questionnaire. Our approach combined an online survey of 337 adults examining authentication practices and perceptions, with a separate usability evaluation in which 49 participants interacted with a simulated ECG-authentication and password interface in a health-care application prototype, then completed a post-test usability test questionnaire. The survey examined how adults across age groups use and perceive various authentication techniques, their experiences with biometrics, their views on security and convenience, and their willingness to adopt ECG authentication. In parallel, the usability prototype and questionnaire captured participants' responses after hands-on interaction with an ECG authentication prototype.

cs.HC · cs.CR · cs.CY · cs.ET · eess.SY

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2609.36485

From Reconnaissance to Response: Quantitative Risk Parameterization and Game Theoretic Containment in Modern Enterprise Attack

Shadeeb Hossain, Elias Calboreanu

原文摘要

Modern Security Operations Centers struggle with delayed manual incident response, enabling adversaries to advance through the Cyber Kill Chain during early stage reconnaissance. While classical game theoretic defense models optimize strategic resource allocation, they rely on static utility matrices that fail to adapt to dynamic telemetry. This paper presents an integrated, metrics driven decision engine that bridges quantitative risk parameterization and continuous automated response time. Common Vulnerability Scoring Systems exploitability parameters are mapped to attacker success probabilities and evaluate defender log distributions via Factor Analysis of Information Risk Monte Carlo simulations. Real time SIEM logs streams are modeled as Poisson process arrival rates, dynamically updating defender posterior threat belief through sequential Bayesian filtering. A closed form threshold is derived by framing the interaction as a dynamic Bayesian Stackelberg game, where the expected unmitigated risk exceeds proactive containment cost. Parameterized against empirical data from the 2023 MGM Resorts and Caesars Entertainment cyber incident, simulation results demonstrate that the engine suppresses transient background noise while triggering automated SOAR network isolation within seconds of adversarial probing. Multi parameter sensitivity analysis confirms that the decision boundary dynamically adjusts to live perimeter vulnerability, offering a control theoretic foundation for sub minute automated threat containment.

cs.GT · cs.CR · eess.SY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39018

Tunable Spatial Task Allocation with Application to Maritime Swarms

Vishnudatta Thota, Tyler Paine, Anastasia Bizyaeva

原文摘要

In this chapter, we explore how a team of autonomous agents can flexibly reconfigure the distribution of its members across many spatially embedded tasks using a task allocation strategy grounded in a generalization of the recently proposed Nonlinear Opinion Dynamics model. Motivated by an adaptive maritime survey application, we couple opinion dynamics directly to spatial movement and examine how the emergent macroscopic allocation is shaped by the interplay between environmental geometry, nature of social interactions, and task demands. We illustrate in simulation how social coupling in the model tunes group cohesion, producing macroscopic allocation patterns ranging from maximally dispersed states, to intermediate semi-dispersed states with a smaller number of distinct subgroups, to maximally cohesive consensus states, in distinct and interpretable parameter regimes. Furthermore, we demonstrate with illustrative examples how a team can reconfigure itself in real time under changing task demands. For allocation scenarios with distance-based task preference and matched number of agents and tasks, we relate numerically the final allocation in the group to the Hungarian optimum, illustrating close-to-optimal performance. We illustrate performance of this method under realistic communication constraints in the MOOS-IvP-Multi simulation environment with simulated autonomous underwater vehicles (AUVs). Together, these examples show how a common set of local interaction rules can support a large range of interpretable macroscopic allocation states and flexible transitions between them.

cs.MA · eess.SY · math.OC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38930

Selective Sliding Mode Control Enhances Robustness of Aerial Robots Under Disturbances

Anoop Kiran, Thales C. Silva, Nora Ayanian

原文摘要

Quadrotor control under disturbances such as wind gusts and payload variations remains challenging. First-order sliding mode control (SMC) and matched/unmatched uncertainty distinction are classical approaches; however, neither tells a practitioner which loops in a cascaded controller should be made robust to sustain disturbances, how to tune those loops, nor what the additional costs are on an embedded processor. Applying robust control uniformly across all axes addresses disturbances indiscriminately, adding tuning effort on axes where no disturbance acts. On inherently underactuated vehicles, disturbances along some axes cannot be opposed directly at all. We propose a principled methodology for deciding where robust control belongs in a cascaded architecture, with all control running onboard the vehicle. We recover the acting disturbance from PID hover flights under disturbance by inverting the equations of motion, use the recovered signal to identify and quantify the excited axes, and deploy robust control where it counts. Experiments on a Crazyflie 2.1 quadrotor under slung-payload and oblique-wind disturbances show tracking improvements concentrated on the axes the rule selects, and gain sweeps confirm that the switching gain reduces error on matched axes but has no effect on an excited unmatched axis, as the analysis predicts.

cs.RO · eess.SY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38888

Inertial Steering and Motion Prediction of Tracked Vehicles on Low-Adherence Surfaces

Mathis Turgeon-Roy, Mohamed Ounally, Veronica Vannini, Olivier Roy, François Pomerleau

原文摘要

Tracked vehicles are widely used in off-road applications, but their conventional motion models often rely on no-slip assumptions that become inaccurate on low-adherence surfaces. Rather than treating inertial effects arising from slip as a disturbance to be minimized, this work investigates how inertial effects arising from a moving mass can be deliberately exploited for steering on low-friction surfaces. A dynamic model for an articulated tracked vehicle with a passive trailer is developed, allowing inertial effects from trailer motion to be captured. The approach is experimentally evaluated on ice, where our dynamic model outperforms the kinematic baseline. We also propose a mixed dynamic-kinematic model to adapt predictions to both surface and situation by exploiting predictions from both methods. Finally, we demonstrate inertial steering on a mono-track vehicle without a conventional steering mechanism, showing that controlled movements of an internal mass can generate turning motion on ice. The resulting behavior can be predicted using the proposed dynamic model by representing the internal mass as a frictionless trailer, revealing the analogous dynamics between the two configurations. These results demonstrate the feasibility of inertial steering as an alternative steering mechanism for tracked vehicles operating in low-adherence environments.

cs.RO · eess.SY

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.36888

Noise Steering: Exploiting Wake Steering and Acoustic Directivity for Noise-Constrained Wind Farm Control via Reinforcement Learning

Martín de Frutos, Baptiste Corban, Claire Bizon Monroc, Ana Bušić, Donatien Dubuc, Jiamin Zhud, Esteban Ferrer

原文摘要

Existing noise mitigation strategies for wind farms usually consist in reducing turbines power output, without accounting for the wake interactions between them and the noise emission directivity. This leads to over-conservative policies and unexploited wind power. Yet, turbine yaw can simultaneously affect wake interactions and the direction of noise emission, offering an additional degree of freedom for farm-level noise control. We therefore propose Noise Steering, a model-free reinforcement learning (RL) strategy that jointly controls turbine yaw angles and power setpoints to maximise farm power while satisfying a local noise constraint in a defined region. To enable training at a reduced computational cost, a low-fidelity acoustic model is coupled with the steady-state FLORIS simulator. The RL agent effectively learns how to redirect wakes and noise directivity away from the protected region while redistributing power across the farm. On a six-turbine test farm, the policy satisfies the 40 dB noise constraint throughout the full range of turbulent winds, with an average power deficit of only 2% relative to greedy unimpeded control, and up to 7.7% at high wind speeds. In comparison, the industry-standard noise-reduced operation (NRO) baseline here generates an average power loss of 17%. When deployed in the higher-fidelity FASTFarm simulator, the policy largely retains its performance under steady and turbulent inflow, despite differences in simulation fidelity. These results demonstrate the potential of combining yaw control and power derating through RL to improve the power-noise trade-off in wind farm operation.

cs.AI · eess.SY · physics.ao-ph · physics.flu-dyn

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38315

LLM-PowerTrace: A Dataset of Time-Resolved GPU Power Traces for AI Compute

Jiansheng Wang, Jaskeerat Singh, Haochen Xu, Xinyi Li, Mingyuan Yan, Shaohui Liu, Yuzhuo Li, Yize Chen 等 9 位作者

原文摘要

AI compute is becoming large and fast-changing electrical loads. Its impacts on data center infrastructure and power grids depend not only on total energy consumption, but also on how their power draw moves over sub-second to minute timescales, including peaks, ramps, and synchronized fluctuations across accelerators. Existing energy benchmarks for large language models (LLMs) report aggregate quantities such as joules per token and rarely release the underlying time-series measurements, leaving these power dynamics poorly characterized and difficult to model. We introduce LLM-PowerTrace, an open dataset of time-resolved, per-GPU power traces for LLM fine-tuning and inference, sampled every 100\,ms on varying NVIDIA platforms paired with complete workload and system metadata. For fine-tuning, LLM-PowerTrace profiles open model families spanning a wide range of model sizes and fine-tuning methods, together with controlled sweeps over batch size, sequence length, precision, parallelism, and checkpointing. For inference, it covers variations in concurrency, GPU frequency, KV-cache and weight quantization, prompt and output lengths, and request-arrival patterns. LLM-PowerTrace profiles a set of open model families across a wide range of model sizes, and controlled sweeps over batch size, sequence length, precision, parallelism, and checkpointing for fine-tuning, and it sweeps concurrency, GPU frequency, KV cache and weight quantization, prompt and output lengths, and request arrival patterns for inference. Built upon power traces, we define power-grid-facing metrics as well as AI-compute power\&energy scaling law for extrapolating beyond the measured settings. Together, LLM-PowerTrace provides a dataset foundation for modeling, forecasting, and managing the fast-changing electrical behavior of AI compute.

eess.SY · cs.AI · cs.OS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38104

Same Label, Different Interface: Beyond Action-Space Labels in Robot Learning

Arshia Sangwan, Zhongyang Zhang

原文摘要

Robot-learning studies often compare position and velocity actions as though each label identified a single experimental treatment, yet the label alone does not determine what the robot executes. Before affecting robot motion, bounded policy output passes through filtering, physical scaling, reference construction, feedback, timing, and effort limits. We study how these omitted properties affect attribution under one shared learner and training budget. Across three contact-rich Factory tasks (PegInsert, GearMesh, and NutThread), we compare five joint-space interfaces, with ten independently trained policies per condition. We first remain within the same position-feedback law and vary the executed interface: substituting only the physical action scale as the directly modified component changes sample-median evaluation success by 0.645-1.000 across tasks, while removing an exponential moving average produces task-dependent changes. We then move to a cross-law comparison and choose a velocity scale that exactly matches the position interface's local raw-torque command gain. This calibration raises the sample median on GearMesh and NutThread, with no median change on PegInsert. Yet clear residual sample-median gaps remain on PegInsert and GearMesh, while the NutThread interval includes zero. Thus, even this explicit equality leaves feedback and held-action dynamics unmatched and does not establish position-velocity trained-outcome equivalence. Action-interface comparisons should therefore report the executed pathway and state the physical quantity they match.

cs.RO · cs.LG · eess.SY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38094

PyPSA-Libya: An Open-Source Energy System Model for National Decarbonization and Hydrogen Export Pathways

Mohamed Abuella

原文摘要

Libya possesses significant renewable energy potential that remains largely untapped. This paper presents PyPSA-Libya, a national-scale model built on the PyPSA-Earth framework, designed to explore optimal transition pathways for the Libyan power sector. We present two primary studies: (1) an electricity-only capacity expansion analysis comparing a zero-emission constraint against an unconstrained baseline at 36-cluster resolution, and (2) a sector-coupled hydrogen export study evaluating the levelized cost of exporting 70 TWh/year of hydrogen to Europe under three technological scenarios and a 100 EUR/tonne CO2 carbon tax regime. Results indicate that while solar PV dominates the domestic capacity mix, integration of hydrogen production significantly alters grid storage requirements. Furthermore, under carbon pricing, steam methane reforming with carbon capture (SMR-CC) frequently out-competes pure green electrolysis, establishing a lower-cost compliance pathway for cross-border export. The model, data, and configuration files are released under an open-source license to support transparent energy planning in Libya.

cs.CE · eess.SY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38079

Neural Compression of Long ADMM Trajectory for Multiparametric Quadratic Program

Liang Wu, Honghui Zheng, Bo Yang, Xu Yang, Yilin Mo, Ján Drgoňa

原文摘要

Solving large-scale multiparametric quadratic programs (mpQPs) in real time often requires thousands of iterative optimization updates, creating a major computational bottleneck in model predictive control and learning-enabled systems. This paper proposes TQPNet, a self-supervised neural compression framework that learns to compress long ADMM optimization trajectories into a compact neural warm start followed by a few refinement iterations. We derive a low-dimensional ADMM scheme operating on the primal-dual variables $(x,β)$ and show that its iteration admits an equivalent ReLU-layer representation, termed ADMM(ReLU). Building on this insight, TQPNet combines a multilayer perceptron with ReLU activations (MLP(ReLU)) and a small number of unrolled ADMM(ReLU) refinement layers. The network is trained offline using an optimality-condition-informed residual loss, eliminating the need for solver-generated labels. The proposed residual-based framework enables both self-supervised training and online solution certification through adaptive ADMM(ReLU) refinement. Experiments on large-scale mpQPs and real-time MPC applications demonstrate that TQPNet achieves high accuracy, strong generalization, and substantially faster inference than conventional optimization-based methods.

math.OC · cs.LG · eess.SY

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.25217

LLM-Driven, Datasheet-Aware Automated Hardware Compatibility Verification for Early-Stage, Pre-Schematic Embedded System Design

Haotian Qiao, Robert P. Dick

原文摘要

We present an LLM-driven, datasheet-aware framework for early-stage hardware compatibility verification that identifies documentation-level interface incompatibilities based on hardware datasheets and high-level component connectivity descriptions. It does not require, and can therefore be used, before detailed schematic simulation and implementation. We view trustworthy LLM-assisted design automation not as directly generating answers from documents, but as transforming engineering information through traceable verification stages. Given hardware datasheets and high-level component connectivity descriptions, the framework constructs a design graph that captures device connectivity and shared interaction domains, retrieves only the engineering properties required by explicit, domain-oriented verification criteria , and generates deterministic scripts for compatibility evaluation. By decomposing compatibility analysis into modular stages and preserving intermediate results, the framework reduces context overhead, improves transparency and tractability, enables scaling, and avoids reliance on LLMs for numerical computation. Evaluated on seven embedded-system designs comprising 34 datasheets, our framework achieves 97.5% compatibility-verification accuracy and an 8.6 times reduction in input context size compared with ``upload-and-query'' workflows. These results demonstrate the feasibility of LLM-assisted, specification-based hardware compatibility verification at an early design stage, as well as the need for, and substantial benefits of, modular task decomposition, formalized verification criteria, and task-aware compact context construction.

cs.AI · eess.SY

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.35040

Context-dependent Runtime Construction of Safe Regions of Attraction from Design-Time Identification for Multi-Candidate Simplex Architectures

Georg Jäger, Nils-Jonathan Friedrich, Norman Seyffer, José Cecílio, António Casimiro

原文摘要

Deploying safety-critical cyber-physical systems requires control strategies that reconcile high performance and adaptability with rigorous safety guarantees. Classical control strategies offer stability proofs but are conservative, while advanced optimization- and learning-based controllers achieve high performance but lack formal verification. Runtime Assurance (RTA) architectures bridge this gap by introducing their own (dis-)advantages. Safety filters use online optimization and introduce chattering, while Simplex permissiveness is bounded by the tightness of the certified Region of Attraction (RoA) of its High-Assurance (HA) controller. Targeting this conservatism, we present a two-stage RoA certification procedure leveraging contraction-based stability to establish a target set for Taylor-model-based reachability analysis. We then treat the closed-loop system under HA control as a switched system, enabling: (a) runtime construction of context-dependent safe RoA and (b) safe switching between HA candidates to optimize recovery and maximize the operating envelope of the uncertified High-Performance (HP) controller. We validate this framework on a robotic path-tracking task using a three-stage evaluation: Ordinary Differential Equation (ODE) simulation for theoretical verification, high-fidelity Gazebo simulation under unmodeled dynamics, and experiments on physical hardware. Results show that the Simplex architecture maintains safety under fault injection and successfully shields a behavioral-cloning-trained HP controller. Crucially, the decision module introduces negligible overhead (0.028 ms average, 4.13 ms worst-case), consuming under 10 % of the control period.

cs.RO · eess.SY

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.36305

Bilinear World Models: Learning Representations with Structured Dynamics for Efficient Control

Antonio Pariente, Ignacio Boero, Nikolai Matni, Alejandro Ribeiro

原文摘要

World models jointly learn latent representations and dynamics that predict how high-dimensional observations evolve under actions. In this work, we propose a JEPA-style world model in which, rather than learning arbitrary latent dynamics, we restrict them to follow a bilinear parameterization. This structure enables efficient planning and control while shifting the modeling burden onto the encoder, encouraging richer representations that expose the controllable geometry of the system. In particular, this structured parameterization allows us to structurally enforce action recoverability, thereby preventing representation collapse by construction. Although prescribing a bilinear parametrization may appear restrictive, we show that a broad class of nonlinear dynamical systems admits a transformation under which the dynamics become bilinear. Empirically, we show across standard 2D and 3D control tasks that representations with bilinear-parameterized dynamics can be learned directly from high-dimensional observations, reducing planning time by nearly three orders of magnitude while retaining or even improving control accuracy. We also propose more demanding regimes of longer-horizon planning and real-time control, and demonstrate that our method succeeds in both, moving JEPA-style world models beyond short-horizon offline planning.

cs.RO · eess.SY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37926

GARDEN: Generalist Autonomous Reward Design Through Evidence Navigation for Cooperative Multi-Agent Reinforcement Learning

Dogan Urgun

原文摘要

Cooperative multi-agent reinforcement learning (MARL) enables teams of agents, such as robots and autonomous vehicles, to learn coordinated behavior, but what such teams learn is largely determined by their reward function. Because task success alone provides too little guidance for learning, intermediate rewards are usually designed by hand, and this effort must often be repeated whenever the environment or the team changes. Large language models (LLMs) can automate the effort by writing reward functions as code, yet the resulting rewards are typically designed and tested in a single setting. To enable their reuse across settings, GARDEN (Generalist Autonomous Reward Design through Evidence Navigation) is proposed, in which one reward program is developed for a family of related scenarios and transferred, unchanged, to scenarios that were not available during its design. During development, environment code, training records, and agent behavior are investigated by an LLM reward engineer, and draft rewards are tested in trial training runs, while the learning algorithm and the rule for selecting the final reward are held fixed. On unseen scenarios of the Overcooked and SMAX benchmarks, task returns were increased by 58.2% and 52.5% over the default rewards on two kitchen layouts, and the mean win rate in two combat scenarios was raised from 51.0% to 89.9%. Under matched candidate and probe budgets, this exceeded the 84.2% and 79.2% obtained with fixed diagnostic feedback and Eureka-inspired reward reflection. These results show that a single autonomously engineered reward can be reused effectively across related cooperative scenarios.

cs.AI · cs.MA · eess.SY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37897

Efficient Joint Trajectory Optimization and Resource Allocation for Secure UAV Networks: A Deep Reinforcement Learning Approach

Abdulmalik Alwarafy, Mohamed Hayajneh, Mustafa Al Juboori, Abdulaziz Almarzooqi, Tahnoon Almazrouei, Robera Abera, Kaleab Nega

原文摘要

Unmanned Aerial Vehicles (UAVs) are increasingly deployed in wireless networks; however, ensuring secure communication and efficient energy usage remains challenging. This paper addresses the joint trajectory optimization and resource allocation problem in UAV networks in the presence of a tethered UAV flying center and a malicious ground eavesdropper. In the proposed model, UAVs are equipped with energy harvesting capabilities and can adaptively switch between transmitting, jamming, and relaying roles to deliver data to a tethered UAV center for optimized network performance. An optimization problem is formulated to maximize a utility function that balances secrecy rate, energy consumption, and interference. To address the high non-convexity of the problem and its real-time decision requirements, a Proximal Policy Optimization (PPO)-based Deep Reinforcement Learning (DRL) algorithm is proposed to solve the problem and learn the network dynamics. Extensive simulation results demonstrate that the proposed approach achieves superior secrecy performance, lower energy consumption, and reduced interference compared to conventional and DRL-based methods.

eess.SY · cs.IT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37534

Computing GR(1) Assume-Guarantee Contracts

Yigit Narter, Emily Zhao, Inigo Incer

原文摘要

Assume-guarantee contracts provide a set of algebraic operations that enable the efficient compositional design and analysis of complex engineering systems. The explicit computation of these operations at component interfaces is currently supported only for some logics lacking a notion of time. This paper develops theory and algorithms to compute these operations when the contracts are expressed in the GR(1) modulo theory. The key challenge is the computation of two quantifier elimination problems for temporal formulas. We develop context-aided variable elimination procedures on GR(1)-modulo formulas, establish their soundness, characterize subproblem-wise optimal solutions within an admissible class of formulas, and use the procedures to compute contract operations, including composition and quotient. We implement the resulting framework in Pacti, automating compositional reasoning over GR(1) contracts. Finally, we demonstrate the framework on an automated emergency-braking case study, where we compute system-level contracts and expose compatibility conditions arising from the interaction of component specifications. Experiments on synthetic families show that the cost is governed by the number of eliminated variables and liveness conjuncts, and a comparison with exact automata-based elimination shows complementary scaling.

cs.LO · eess.SY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37452

Electric-Field Induction Motor with Discrete Circuit Elements and Improved Rotor Dynamics

Alex S. Miller, Jeffrey H. Lang

原文摘要

Electric-field-based actuators and machines lead the design space at the nanoscale and in biological systems, while having the potential to be used in favorable macroscale applications which prioritize minimal mass, performance in a vacuum, or performance in high temperatures. Electric-field induction motors consist of a set of stator electrodes which induce opposite charges onto a movable rotor surface. In earlier work, rotor surfaces for electric-field induction motors have been made of single continuum materials, which are limited in possible resistivity, permittivity and circuitry characteristics. To enhance design freedom, we develop a rotor for electric-field-based machines which may be constructed out of discrete circuit components between a finite number of conductive vanes. Magnetic-field induction motors, which are common in machinery, often utilize ``deep bar'' rotors, consisting of conducting bars which decrease the rotor time constant with increasing frequency using the skin depth effect. We propose, analyze, design and demonstrate a macro-scale electric-field induction motor which uses circuitry on the rotor surface to emulate the ``deep bar'' design, providing improved torque over a wide range of speeds.

cs.RO · eess.SY

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.02466

SD-DPC: Sparse Dictionary Differentiable Predictive Control

Ali Reza Daneshvar Garmroodi, Jan Drgoňa

原文摘要

We present sparse dictionary differentiable predictive control (SD-DPC), a framework for learning sparse, interpretable feedback policies for nonlinear systems from data. A prediction model is first identified by rollout-based sparse identification of nonlinear dynamics (SINDy), building on gradient-based and multistep formulations. The policy is then parameterized as a sparse combination of dictionary functions and trained by differentiating a constrained finite-horizon predictive-control objective through this model, so that its terms are selected by closed-loop performance rather than by imitating a previously trained controller. The result is an explicit feedback law with only a handful of terms. Across three benchmark control problems, SD-DPC satisfies the constraints in almost all test scenarios, outperforms a policy distilled onto the same terms by up to an order of magnitude, and requires orders of magnitude less memory and online computation than an optimization benchmark, while admitting explicit sensitivity bounds.

eess.SY · cs.LG · math.OC

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arXiv ↗PDF ↗
版本更新 · v9arXiv:2507.04346

Action-Increment-Constrained Policy Optimization for Smooth Online Adaptive Critic Flight Control with Actuator Rate Constraints and Low Computational Complexity (extended version with Ultimate Uniform Boundedness analysis)

Yifei Li, Erik-Jan van Kampen

原文摘要

Approximate Dynamic Programming (ADP) enables online control-law adaptation through adaptive critic methods. A safety challenge in applying ADP to angle-of-attack (AoA) tracking control is that an overly sensitive neural network (NN) controller may generate oscillatory or saturated control actions under nonlinear dynamics, potentially compromising flight stability and increasing actuator stress. To address this challenge, this paper proposes two innovations: (1) the actuator rate constraints are enforced within a an Action-Increment-Constrained Policy Optimization (AI-CPO) framework inspired by safe reinforcement learning (RL), in which a primal-dual approach adaptively adjusts the penalty weight to address actuator rate constraints. This optimization is solved online using adaptive Temporally Smoothed Incremental Model-Based Heuristic Dynamic Programming (TS-IHDP) for smooth and computationally efficient online adaptive control; and (2) a low-pass filter integrated into the cascaded NN architecture to attenuate high-frequency pitch-rate commands while preserving the Markov property during online learning by exploiting the hierarchical actor structure. Compared with the IHDP baseline, simulations using a longitudinal air vehicle dynamics model demonstrate that TS-IHDP improves control smoothness and tracking performance, reduces system oscillations, and enhances the trained policy's robustness to model uncertainties.

eess.SY · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37424

Privacy-Preserving Over-the-Air Federated Learning over Cell-Free Massive MIMO System with User-Centric Clustering

Manobendu Sarker, Soumaya Cherkaoui

原文摘要

This paper investigates cross-layer design for differentially private federated learning over cell-free massive MIMO networks with over-the-air (OTA) aggregation. Distributed access points (APs) collaboratively serve user equipments (UEs) through user-centric clustering, where clipped local model updates are perturbed with Gaussian noise and simultaneously transmitted over a shared uplink for OTA averaging at a central processing unit. Exploiting channel hardening, we show that thermal noise inherent in wireless reception can act as a privacy-masking resource, reducing or even eliminating the need for artificial noise. Based on this observation, we formulate a joint optimization problem over AP-UE associations, UE transmit powers, and artificial noise variances to minimize aggregation variance subject to per-UE zero-concentrated differential privacy constraints derived from an ergodic privacy accounting framework. Analysis reveals a regime-dependent structure: stringent privacy requirements favor low transmit powers and sparse AP associations to exploit thermal noise for privacy, whereas relaxed requirements favor larger clusters to improve aggregation quality. The resulting mixed-integer nonconvex problem is solved via alternating optimization combining difference-of-convex power allocation and greedy coordinate-descent clustering. Numerical results show up to 89% lower aggregation variance than conventional baselines and enable 97% test accuracy on MNIST with consistent gains on CIFAR-10. Under strict privacy budgets, competing schemes remain near random-chance performance, while under relaxed budgets they continue to lag significantly behind the proposed design.

cs.IT · eess.SY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37310

Spiking Neuromorphic Control for Periodic Orbits Stabilization in Underactuated Robots: A Greedy Lyapunov-Based Approach

Maksim Surov, Oleg Sumenkov

原文摘要

This work investigates the use of event-based spiking controllers, implemented as spiking neural networks, for trajectory tracking in underactuated robots. Specifically, we address the problem of stabilizing a prescribed periodic orbit. It is well known that, by applying the method of transverse linearization to the robot dynamics in a neighborhood of a periodic trajectory, the dynamics can be approximately described by a linear time-varying system with periodic coefficients (a periodic LTV system). The problem of orbital stabilization can therefore be reduced to stabilizing the trivial solution of this LTV system. We show that a previously proposed Lyapunov-based greedy design, originally developed for LTI systems, can also be extended to the corresponding linearized transverse dynamics, which take the form of a periodic LTV system. This enables the construction of a feedback controller for orbital stabilization of the robot. The effectiveness of the proposed method is demonstrated on the trajectory stabilization problem for the butterfly robot.

cs.RO · eess.SY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37199

A Bi-level Co-design Framework for Lunar Monoped with 2D Jump and Landing Control

Sanjay Ramkumar, Suraj Kumar

原文摘要

Legged robots for lunar exploration must be designed for low gravity, where reduced contact forces and long flight phases make leg morphology and controller choice interdependent. We study this for a planar monoped in lunar gravity ($1.625$ m/s$^2$) using a bi-level co-design framework: a differential evolution outer loop searches over link lengths, and each candidate is evaluated on a vertical jump by either a quadratic program based task-space controller (QP-TSC) or a direct-collocation based trajectory optimization. The two controllers yield different optimal link lengths, and trajectory optimization outperforms the QP-TSC across most of the design space, with 17% lower Cost of Transport (COT) at their respective optima. We then extend to a 2D jump, where trajectory optimization with centroidal angular momentum regulation produces stable in-place and forward jumps, and an LQR controller absorbs impact and stabilizes the robot after touchdown. This is demonstrated on a baseline morphology with both links 0.3 m long.

cs.RO · eess.SY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36996

Structure-Actuation Co-Design of All-Electric Heavy-Duty Robotic Manipulators

Mohammad Bahari, Amir Hossein Barjini, Jouni Mattila

原文摘要

This paper presents a mixed discrete-continuous multiobjective bilevel framework for the structure-actuation co-design of all-electric heavy-duty robotic manipulators. The upper level optimizes manipulator geometry and actuation topology, while joint-specific lower-level searches construct feasible archives of discrete motor, gearbox, and screw configurations. A system-level coordinator selects actuator packages according to hardware mass, co-design cost, and loss-aware supply energy, and the resulting actuator masses are fed back to the robot model until a mass-consistent fixed point is obtained. Evaluation on a full-pose SE(3) benchmark with a 100-kg tool payload yields eight feasible nondominated designs, all satisfying the 12 prescribed poses. Actuator-mass coupling converges in three iterations for every reported design. A fixed-design ablation shows that one-pass sequential sizing changes hardware mass by up to 2.13%, co-design cost by 10.20%, and supply energy by 9.40% in magnitude. Payload screening further reveals increasing mass-cost-energy demand and changes in competitive mechanism topology with carried load.

cs.RO · eess.SY

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2504.11811

Manifold meta-learning for reduced-complexity neural system identification

Marco Forgione, Ankush Chakrabarty, Dario Piga, Matteo Rufolo, Alberto Bemporad

原文摘要

System identification has greatly benefited from deep learning techniques, particularly for modeling complex, nonlinear dynamical systems with partially unknown physics where traditional approaches may not be feasible. However, deep learning models often require large datasets and significant computational resources for both training and inference due to their high-dimensional parameterizations. To address this challenge, we propose a meta-learning framework that discovers a low-dimensional manifold within the parameter space of an over-parameterized neural network architecture. This manifold is learned from an abundant meta-dataset of input-output sequences generated by a simulator of the class of systems of interest, and thereafter enables efficient model training while preserving the network's expressive power for the considered system class. Unlike bilevel meta-learning approaches, our method employs an auxiliary neural network to map datasets directly onto the learned manifold, eliminating the need for costly second-order gradient computations during meta-training and generally reducing the number of first-order updates required in inference, which could be expensive for large models. Finally, we derive a probabilistic bound that quantifies the performance gap between training on the learned low-dimensional manifold and the full parameterization. We validate our approach on a family of Bouc-Wen oscillators, which is a well-studied nonlinear system identification benchmark. We demonstrate that we are able to learn accurate models even in small-data scenarios.

cs.LG · eess.SY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36812

Gradient flow of magnetic inclination and intensity as a GPS-free guidance mechanism

Zayed Herma, Philippe Cardin, Andréa Vassilev

原文摘要

It is well established that some animals, such as sea turtles, sense the Earth's magnetic field and use it as a primary cue for long-distance navigation across the vast and featureless ocean. This remarkable ability has inspired the development of bio-inspired magnetic navigation (BIMN), which seeks to mimic these animals to develop robust and frugal navigation methods. BIMN exploits the Earth's magnetic field, sometimes described as ''Nature's GPS'', as a natural navigation infrastructure, providing a potential alternative to vulnerable satellite navigation systems. However, unlike geographic coordinates, magnetic coordinates extracted from the Earth's magnetic field do not provide a one-to-one mapping to geographic locations. Different locations can exhibit similar magnetic signatures, creating ambiguity and making BIMN inherently more challenging. Despite this fundamental challenge, we demonstrate that, within predefined regions of the Earth, magnetic inclination and intensity provide sufficient information to navigate to any destination. By formulating BIMN as a gradient flow control and guidance problem, we show that the resulting gradient flow act as current that carries the system and can be controlled to guide it through intermediate waypoints toward the desired destination. The resulting framework provides a GPS-free navigation mechanism with potential applications in both engineering and biology. In engineering, it can be used to establish a geomagnetic navigation infrastructure through which prescribed routes can be defined and followed, while in biology, it can be used to analyse the migration routes of animals known to rely on magnetoreception.

cs.RO · eess.SY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36683

Feedback autocratic strategies in stochastic games

Masahiko Ueda

原文摘要

A stochastic game is an optimal control problem for multiple agents in a stochastic environment. Stochastic games contain important practical problems, related to environmental issues or traffic flows. If the number of environmental states is one, such stochastic games are called repeated games. In repeated games, several strategies which unilaterally control payoffs have been proposed, and these strategies work in a mechanism similar to feedback control. In this paper, we generalize these strategies as feedback strategies in stochastic games, and investigate their properties. In particular, we provide a necessary and sufficient condition for such feedback strategies to work as controllers of payoffs under some assumptions. We demonstrate the performance of such feedback strategies in several examples related to social dilemmas. Our findings connect the theory of payoff control in repeated games to the theory of feedback control in stochastic games.

cs.MA · eess.SY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36430

A Singularity Avoiding Track Model for Race Car Optimization

Adwait Verulkar, Behzad Danaei, Srikanth Sivaramakrishnan, Ryan Trinter

原文摘要

A track fitting pipeline is presented that converts a measured racetrack point cloud into a closed, $C^4$ differentiable, three-dimensional surface model suitable for high-fidelity Modelica race-car optimization. The surface is a Monge patch, parametrised by a curvilinear arc-length coordinate measured along the minimum-curvature line (MCL) and a signed lateral offset, with a height polynomial of user-selectable degree capturing the cross-sectional elevation profile. The choice of MCL as the reference line is deliberate since anchoring the same surface on the geometric centre-line can cause adjacent normals to cross inside the drivable region on tight corners. The resulting non-injective parametrisation assigns multiple curvilinear addresses to the same three-dimensional point and returns invalid road geometry. The MCL is obtained from a quadratic program that minimises integrated curvature subject to track-boundary, periodicity, and jerk-regularisation constraints. By reducing total curvature while remaining within the track bounds, the MCL moves the region of parametrisation breakdown outside the drivable strip on all circuits studied, keeps the normal map injective across the full track width, and yields a more linear time-to-arc-length transformation for minimum lap-time problems. The fitted surface and the associated track widths are exported as periodic piecewise-quintic $C^4$ B-splines, solved as a nonlinear program.

cs.RO · eess.SY

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