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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.
Two headroom-aware adaptive MPC strategies are developed for PV-dominated microgrids, accounting for stochastic and time-varying reserve headroom of inverter-based resources to avoid control saturation and improve load frequency control.
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.
