ModellingStochastic Adaptive MPC for Load Frequency Control in MicrogridsarXiv — Systems and Control (eess.SY) · yesterdayForecastingCritical Review of LLMs for HVAC Operations in BuildingsarXiv — Systems and Control (eess.SY) · yesterdayDigital twinCyber-physical systems lab opens at Voronezh State UniversityВоронежский госуниверситет · 21hDigital twinAI-Powered CPS Urban Transportation Digital TwinarXiv — Systems and Control (eess.SY) · yesterdayModellingSelectel and ITMO JV develops multi-agent system technologies for businessspbIT · yesterdayForecastingGrid-Aware MPC for Smoothing AI Data Center Power DemandarXiv — Systems and Control (eess.SY) · yesterdayEnergyComparison of Direct vs Indirect Net Load Forecasting with RenewablesarXiv — Systems and Control (eess.SY) · yesterdayDigital twinBayesian thermal digital twin for space habitat under impactarXiv API — полнотекстовый поиск "digital twin" · 4dDigital twinData-driven transient forecasting for generator digital twin decision supportarXiv API — полнотекстовый поиск "digital twin" · 3dMarket & productsKorea and Africa Discuss AI and Digital Infrastructure at KOAFECAfDB Africa Newsroom (пресс-релизная витрина) · 4dModellingSeed-Anchored Graph Rendering for QA on Power-Grid CIM/CGMES ModelsarXiv — Systems and Control (eess.SY) · 5dDigital twinDNative-Twin: Graph-Native Digital Twins for Reconstructable Agentic DecisionsarXiv API — полнотекстовый поиск "digital twin" · 4d
Iterative MPC without Derivatives for Constrained Nonlinear Systems
US & West ·
An iterative MPC algorithm stabilizes constrained nonlinear systems without computing derivatives. The method linearizes dynamics via state- and control-dependent coefficients and solves a sequence of linear-quadratic programs, proving convergence near the origin.
Why it matters
Offers an efficient way to control nonlinear systems, important for industrial applications where derivatives are hard to obtain.
A systematic review of 66 studies (2023-2026) on LLMs for HVAC operations classifies methods and applications, revealing concentration on building energy modeling but few pilot-level deployments and no sustained operational use.
Methods for urban traffic digital twins are presented, emphasizing prediction and decision-making capabilities, powered by AI and cyber-physical systems, distinguishing them from traditional simulators.
A joint venture between Selectel and ITMO, Emergent Multi-Agent Systems LLC, develops technologies for creating and operating multi-agent systems. The CEO discusses managing large fleets of AI agents and their reliability and cost-effectiveness.
