Digital twinDigital Twin for Pre-Deployment Validation of AI-Driven Safety-Critical Industrial Edge Control LoopsarXiv API — полнотекстовый поиск "digital twin" · 2dDigital twinLeveraging Human-In-The-Loop Demonstrations in Reinforcement Learning for Digital Twin-Driven Robot FlexibilityarXiv API — полнотекстовый поиск "digital twin" · 2dDigital twinLIVIN: Benchmarking Spatial and Embodied Intelligence in Digital Twins of Lived-In HomesarXiv API — полнотекстовый поиск "digital twin" · 2dModellingA dynamic pore-network model with discrete microbubble transportarXiv — Computational Engineering, Finance, and Science (cs.CE) · 2dModellingFREIDA: A Framework for developing quantitative agent based models based on qualitative expert knowledgearXiv — Multiagent Systems (cs.MA) · 2dModellingCooperation, Defection, and the Commons: Reproducing an Agent-Based Model of Aksum with Three Logistic EquationsarXiv — Physics and Society (physics.soc-ph) · 2dDigital twinRuleless digital twins: declarative decision-making via standardized frameworksarXiv API — полнотекстовый поиск "digital twin" · 3dDigital twinPredictive cognitive digital twin framework for autonomous 6G networksarXiv API — полнотекстовый поиск "digital twin" · 3dDigital twinAdjoint-based calibration and optimal control of stochastic bioprocess twinsarXiv API — полнотекстовый поиск "digital twin" · 3dDigital twinStrategic Evaluation of Planning Strategies for LLM Agents in Cyber-Physical SystemsarXiv — Multiagent Systems (cs.MA) · 3dForecastingModel Predictive Control for Safety-Critical Systems Using Taylor's Theorem with Lagrange RemainderarXiv — Systems and Control (eess.SY) · 3dEnergyBenchmarking Time Series Foundation Models for Load Forecasting Under Covariate UncertaintyarXiv — Systems and Control (eess.SY) · 3d
Information-Aware Model Predictive Control for Satellite Inspection
US & West ·
A MPC framework for autonomous satellite inspection incorporates estimation covariance into the control objective, merging control and state estimation. The covariance evolves via a linear Kalman filter, and the measurement model depends on relative geometry. This optimizes trajectories for informative data collection while satisfying safety constraints.
Why it matters
The approach demonstrates integration of estimation and control to improve data collection efficiency, relevant for asset monitoring and control tasks.
Industrial environments are increasingly characterized by the tight interaction among physical processes, communication infrastructures, and intelligent applications. In this context, Digital Twins (DTs) have emerged as a key technology for system analysis and optimization.
A ruleless digital twin approach is proposed where decisions are generated automatically from purely declarative user specifications instead of hand-written rules. This reduces the complexity of building and maintaining twins under dynamically changing conditions such as weather or dynamic energy pricing.
A calibration and control method is developed for a multiscale bioprocess digital twin using sparse observations, quasi-likelihood estimation and adjoint sensitivity analysis. Parameter uncertainty then feeds into policy optimization and adaptive experiment design.
