Digital twinML-assisted Bayesian Calibration of Accelerator Digital TwinarXiv API — полнотекстовый поиск "digital twin" · 15hDigital twinDigital Twin Testbed for Cyberattack and Defense Analysis in Hospital IT/OT EnvironmentsarXiv API — полнотекстовый поиск "digital twin" · 18hModellingGenerative Agent-based Simulation of Mobility Policy Preferences from SurveysarXiv — Computers and Society (cs.CY) · yesterdayForecastingScalable Gaussian Process with Trigonometric Features for Safe MPCarXiv — Systems and Control (eess.SY) · yesterdayForecastingTwo-Layer MPC for Sustainable Data Centers with Workload Flexibility and Heat RecoveryarXiv — Systems and Control (eess.SY) · yesterdayForecastingIterative MPC without Derivatives for Constrained Nonlinear SystemsarXiv — Systems and Control (eess.SY) · yesterdayForecastingDisturbance-adaptive MPC with Bounds on Average Constraint ViolationsarXiv — Systems and Control (eess.SY) · yesterdayDigital twinDigital Twin Degradation: Detecting Cyber Physical Attacks via Temporal InconsistenciesarXiv API — полнотекстовый поиск "digital twin" · yesterdayForecastingControl Architecture for Fast Frequency Regulation with High Inverter-Based Resource PenetrationarXiv — Systems and Control (eess.SY) · 2dDigital twinDigital Twin-Based Intrusion Detection for Vehicle Powertrain CAN BusarXiv API — полнотекстовый поиск "digital twin" · yesterdayDigital twinPRISM: Decision-Centric Predictive Sensing for Cognitive Digital Twins in 6GarXiv API — полнотекстовый поиск "digital twin" · yesterdayModellingNeural Network-Based Parameter Estimation for a Labour Market ABMarXiv — Multiagent Systems (cs.MA) · 2d
Disturbance-adaptive MPC with Bounds on Average Constraint Violations
US & West ·
A new DAD-MPC framework for stochastic systems adapts the disturbance model based on measured constraint violations. The method guarantees recursive feasibility and asymptotic bounds on average violations even with an inaccurate model, enabling data-driven approaches like conformal prediction.
Why it matters
Important for constrained control under uncertainty, relevant to energy and industry.
A framework converts real survey respondents into LLM agents for simulation. This approach improves realism of behavioral simulations and can be used for experiments in mobility policy.
Researchers developed DTF-GP, a finite-dimensional kernel approximation based on deterministic trigonometric features. This approach enables uniform uncertainty bounds needed for safety guarantees in learning-based MPC while scaling to large datasets.
A two-layer MPC framework for data centers with PV, battery, and heat recovery is proposed. The upper layer optimizes market participation and workload scheduling, while the lower layer compensates disturbances using multi-horizon forecasts.
