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Collective Tube MPC With Distribution-Free Joint Safety Certificates
US & West ·
The paper develops collective tube MPC, where the calibrated uncertainty object is the entire finite-horizon prediction-error trajectory rather than separate events combined by a union bound. A reusable trajectory tube is calibrated offline, its cross-sections define online Pontryagin tightenings, and the certified violation event is a fresh prediction-error trajectory leaving the tube.
Why it matters
The approach aligns safety certificates with the recursive-feasibility logic of tube MPC, which matters for reliable control of constrained systems.
The National University of Singapore (NUS) and the Society of Algorithmic Intelligence (SoAI) is hosting IntelligenceX 2026: Global Quantum × AI Frontier, from 24 to 26 September 2026 at NUS University Town, focused on advances at the intersection of quantum computing and artificial intelligence…
This study evaluates a physics-informed neural network for potential-temperature forecasting under incomplete thermal observations, constrained by a pressure-coordinate advection-source equation and a diabatic-source closure frozen after the preceding 12 hours. Validation uses hourly ERA5 reanalysis at three pressure levels for one-, two- and three-hour horizons against persistence, local-trend, and two neural baselines.
A trust-region formulation is proposed for sampling-based model predictive control algorithms such as MPPI, constraining updates of the proposal distribution via a KL divergence bound and, optionally, an entropy lower bound. This replaces heuristic tuning of temperature or momentum with values optimal with respect to the underlying Lagrangian.