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Topological Feasibility Guarantees for Differentiable Predictive Control
US & West ·
The paper establishes deterministic feasibility guarantees for differentiable predictive control (DPC), a self-supervised approach for approximating explicit MPC policies. Through topological analysis of the induced reachable safe set, it shows DPC can guarantee feasibility without online safety filters.
Why it matters
Addressing feasibility guarantees for learned control policies extends the applicability of machine learning methods in safety-critical control systems.
The article discusses an architecture of a digital twin for nuclear systems, integrating multiple models (physics-based and data-driven) to support decision making, state estimation, predictive control, and real-time data processing. The twin must synchronize with the physical facility faster than its operational cycle.
A new stochastic nonlinear model predictive control method for systems with additive noise is proposed. State distribution is approximated by Gaussian mixture with error bounds in Wasserstein distance. This yields closed-form expressions for expected costs and chance constraints, and the problem is solvable via nonlinear programming with correctness guarantees.
Coordinated protests against Palantir took place in eight US cities, organized by nurses, patients, and activists. They demand that hospitals and authorities terminate contracts with Palantir due to concerns about surveillance, including its use by immigration services, and the company's growing presence in healthcare.
