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Scalable Gaussian Process with Trigonometric Features for Safe MPC

US & West · 18 Aug

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.

Why it matters

Important for scalable learning-based control with safety guarantees in large-scale systems.

Original headline: Scalable Gaussian Process Regression via Deterministic Trigonometric Features: Uniform Bounds for Safe Model Predictive Control

Read the original: arXiv — Systems and Control (eess.SY)

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