287 papers
This paper proposes a modular supervisory control approach for discrete event systems to reduce computational complexity and enhance the robustness of predicting and mitigating power system cascading failures, demonstrating its effectiveness through simulations on IEEE 30-bus, 118-bus, and 300-bus test systems.
This paper introduces DMaC, a novel distributed algorithm that guarantees finite-time max-consensus in directed graphs with unreliable communication links by leveraging narrowband error-free feedback for acknowledgments and a fully distributed termination mechanism to ensure exact convergence under arbitrary packet loss.
This paper proposes a method for estimating failure rates in stationary periodic Rate Monotonic real-time systems by approximating response time distributions with an inverse Gaussian mixture model, whose parameters are derived using a re-parameterized Expectation-Maximization algorithm and validated through extensive simulations.
This paper presents a reference architecture and roadmap for Quantum-Centric Supercomputing (QCSC) systems that integrate quantum, GPU, and CPU resources to overcome current isolation challenges and enable seamless, high-performance hybrid workflows across three evolutionary phases.
This paper presents a novel robotic gripper featuring an active dented-roller fingertip and compliant fingers that reliably separates and opens sterile medical pouch flaps, offering a solution to automate a physically demanding and injury-prone manual task performed by hospital staff.
This paper proposes distributed dynamical algorithms that enable agents with only local data and no raw data sharing to collectively compute global system certificates, specifically solving the Lyapunov equation for stability verification and the algebraic Riccati equation for optimal LQR control, with guarantees of exact convergence and robustness.
This paper proposes a hierarchical Graph Attention Network (HR-GAT) model that leverages public deployment records to accurately estimate fine-grained spectrum demand across multiple cities, significantly outperforming existing baselines and providing regulators with actionable insights for efficient spectrum sharing and allocation.
This paper proposes an AI-driven framework that improves 5G/6G traffic demand prediction accuracy and spatial generalization by employing a context-aware two-stage splitting strategy and residual error correction to mitigate neighborhood leakage, as validated by experiments across five Canadian cities.
This paper presents a fully GPU-native trajectory optimization framework that leverages sequential convex programming and consensus-based ADMM with temporal splitting to achieve real-time, high-throughput nonlinear optimal control for autonomous systems, demonstrating significant speedups and energy efficiency over CPU baselines while enabling scalable multi-trajectory and robust Model Predictive Control.
This paper presents a dynamic model and control strategy for an underactuated bipedal hopping robot that utilizes an internal reaction wheel to stabilize body posture during ballistic flight under low-gravity conditions, successfully reducing mid-air angular deviation by over 65% and ensuring precise upright landings in lunar gravity simulations.
This paper establishes theoretical conditions under which Monotone Operator Equilibrium Networks maintain convergence and bounded error under weight quantization by analyzing spectral perturbations, and validates these findings through experiments showing that quantization-aware training can recover provable convergence at four-bit precision.
This paper proposes a world model framework for lithium-ion battery degradation prognosis that encodes cycle data into latent states and propagates them forward using learned dynamics, demonstrating that iterative rollout significantly reduces trajectory forecast error compared to direct regression while a Single Particle Model constraint specifically enhances prediction accuracy at the degradation knee.
This paper introduces Concept-Gated Visual Distillation (CGVD), a training-free, model-agnostic inference framework that overcomes the "Precision-Reasoning Gap" in Vision-Language-Action models by parsing instructions to identify distractors and using Fourier-based inpainting to generate clean observations, thereby significantly improving robotic manipulation success rates in highly cluttered environments.
This paper proposes a method to immerse non-polynomial Port-Hamiltonian systems into higher-dimensional polynomial representations that preserve key structural and energy properties, thereby enabling the design of stabilizing feedback laws through sum-of-squares optimization and passivity-based control.
By analyzing real-world Californian BEV usage data to identify distinct driver profiles, this study demonstrates that Vehicle-to-Grid (V2G) adoption is most viable for "Daily Chargers" and that its impact on battery lifetime varies significantly based on calendar aging sensitivity and charging habits, ranging from increased capacity loss to potential retention improvements.
This paper proposes a distributed safety-critical control framework for multi-agent systems with uncontrollable agents, utilizing a novel reconstructed Control Barrier Function based on distributed adaptive state estimates and prescribed performance parameters to decouple safety constraints and rigorously guarantee system safety.
This paper establishes a control-theoretic framework that formalizes agentic systems as hierarchical decision authorities within feedback loops, providing a unified dynamical representation to analyze the stability, safety, and performance of AI agents that adapt controllers, strategies, and objectives in real-time.
This paper demonstrates that applying dimensional analysis and data scaling to large-scale agent-based simulations of autonomous drone swarms reveals predictable, counterintuitive scaling laws and sharp success-failure boundaries, enabling rapid, budget-aware optimization of agent counts, platform parameters, and path planning strategies across diverse mission scenarios.
This paper proposes a neural operator integrated with an implicit numerical scheme that learns underlying state-space dynamics from limited data to accurately predict vibration frequency response curves for engineered components without relying on physics-based regularizing loss functions.
This paper proposes a distributed state estimation scheme for discrete-time LTI systems that utilizes the Jordan canonical form to combine local Luenberger observers with consensus-based strategies, thereby establishing necessary and sufficient conditions for asymptotic convergence while offering greater flexibility and less restrictive solvability conditions than previous work.