302 papers
The paper introduces the Frequency-Separable Hamiltonian Neural Network (FS-HNN), a novel architecture that decomposes Hamiltonian functions into distinct fast and slow modes trained on different timescales to overcome the spectral bias of existing methods and significantly improve long-horizon extrapolation for multi-timescale dynamical systems and PDEs.
This paper introduces CLAIRE, a hybrid deep learning framework that combines unsupervised latent representation learning with supervised classification and game-theoretic interpretability to achieve robust, explainable fault detection in high-dimensional industrial environments.
This paper introduces "control barrier corridors," a unified framework that converts control barrier functions into local safe goal regions to bridge the gap between functional safety filtering and geometric safe motion planning, enabling verifiably safe and persistent path following in complex, unknown environments.
This paper proposes an adaptive data-driven min-max model predictive control scheme for discrete-time linear time-varying systems that utilizes online input-state data to update state-feedback gains via semidefinite programming, ensuring exponential stabilization and constraint satisfaction for both noise-free and noisy scenarios.
This paper extends a hardware-efficient hybrid digital-analog RIS architecture by proposing a pragmatic phase-only beam design for hierarchical codebooks and a low-complexity multiuser MIMO framework that achieves high spectral efficiency under realistic mmWave multipath channels while maintaining full compliance with 3GPP 5G NR standards.
This paper presents a novel uncertainty-aware adaptive dynamics framework for underwater vehicle-manipulator systems that employs moving horizon estimation with convex physical constraints to achieve rapid, online parameter convergence and high-fidelity modeling, as validated by experiments on a BlueROV2 Heavy platform.
This paper proposes and experimentally validates a conductive amplitude and phase matrix framework that enables the emulation of multiple targets with arbitrary radar profiles for testing integrated sensing and communication (ISAC) base stations equipped with large-scale antenna arrays using radar target simulators with limited interface ports.
This paper introduces DRAFTO, a novel trajectory optimization algorithm for robotic manipulators that decouples reduced-space Gauss-Newton descent with adaptive feasibility repair to efficiently generate smooth, safe, and constraint-compliant paths, demonstrating superior performance over existing planners in diverse and complex manipulation tasks.
This paper introduces TATIC, a unified framework that combines torque-based force estimation with a task-aware Temporal Convolutional Network to infer both discrete task-level intent and continuous motion parameters from brief physical corrections, enabling robust online adaptation in human-robot collaboration.
This paper investigates the impact of parameter selection on the convergence of a modified ant colony algorithm to optimize the hierarchical structure of an industrial distributed control system, offering tuning recommendations applicable to broader combinatorial optimization problems.
This paper extends the Irreversible Port-Hamiltonian Systems (IPHS) framework to model non-isentropic fluids with viscous dissipation in both Eulerian and Lagrangian coordinates by adapting differential operators for convective transport and defining boundary controls that satisfy the first and second laws of thermodynamics.
This paper proposes a novel adaptive algorithm for multi-robot multitask coverage that combines a federated control strategy with a multitask Gaussian Process framework to learn unknown sensory demands, achieving sublinear cumulative regret compared to an oracle with prior knowledge.
This paper extends irreversible port-Hamiltonian system formulations from one-dimensional to N-dimensional boundary-controlled distributed parameter systems, providing a unified, thermodynamically consistent framework for modeling conduction-diffusion phenomena that preserves energy balance and entropy production while enabling structure-preserving numerical control.
This paper proposes an enhanced Distributed Kalman-Consensus Filter for multi-object tracking in mobile robot networks that combines the MOTLEE framework's frame-alignment methodology with a novel adaptive uncertainty weighting mechanism to dynamically mitigate the impact of heterogeneous localization errors and communication latency, resulting in improved tracking accuracy.
This paper establishes conditions under which multi-stage Runge-Kutta methods preserve strong contractivity for infinitesimally contracting continuous-time systems, deriving coefficient-dependent criteria for explicit schemes and extending classical implicit guarantees to strong contractivity across , , and norms while ensuring unique solvability via an auxiliary dynamic system.
This paper proposes SliceFed, a novel Federated Constrained Multi-Agent Deep Reinforcement Learning framework that leverages a Lagrangian primal-dual approach with Proximal Policy Optimization to optimize dynamic spectrum slicing in 6G networks, achieving near-perfect URLLC latency compliance and robust interference management while preserving data privacy through federated learning.
This paper introduces KProxNPLVM, a novel nonlinear probabilistic latent variable model that employs Wasserstein distance-based proximal relaxation to eliminate the approximation errors inherent in conventional amortized variational inference, thereby significantly improving soft sensor modeling accuracy.
This paper proposes a low-complexity iterative algorithm for an ISAC-enabled multi-UAV collaborative target sensing scheme that jointly optimizes UAV-BS association, trajectories, and bandwidth allocation to minimize the posterior Cramer-Rao bound for tracking dynamic low-altitude targets while satisfying communication constraints.
This paper proposes a multi-agent deep reinforcement learning framework utilizing virtual anchor nodes for unmanned aerial vehicles to achieve superior accuracy and efficiency in localizing chemical plume sources from undocumented orphaned wells compared to traditional fluxotaxis methods.
This paper proposes a novel rotatable antenna (RA)-enabled covert communication system that jointly optimizes transmit beamforming and RA rotational angles via an alternating optimization algorithm to maximize covert rate while satisfying covertness and power constraints, demonstrating superior performance over benchmark schemes.