activity
20202023
most citedKPC: Learning-Based Model Predictive Control with Deterministic Guarantees

11 citations · 12 across the 5 of their papers we have counts for

collaborators

5 papers

eess.SY2023★ 1 cited

Risk-aware Scheduling and Dispatch of Flexibility Events in Buildings

Paul Scharnhorst, Baptiste Schubnel, Rafael E. Carrillo +2

Residential and commercial buildings, equipped with systems such as heat pumps (HPs), hot water tanks, or stationary energy storage, have a large potential to offer their consumpti…

eess.SY2022

Uncertainty-aware Flexibility Envelope Prediction in Buildings with Controller-agnostic Battery Models

Paul Scharnhorst, Baptiste Schubnel, Rafael E. Carrillo +2

Buildings are a promising source of flexibility for the application of demand response. In this work, we introduce a novel battery model formulation to capture the state evolution…

cs.LG2021

Robust Uncertainty Bounds in Reproducing Kernel Hilbert Spaces: A Convex Optimization Approach

Paul Scharnhorst, Emilio T. Maddalena, Yuning Jiang +1

The problem of establishing out-of-sample bounds for the values of an unkonwn ground-truth function is considered. Kernels and their associated Hilbert spaces are the main formalis…

eess.SY2020★ 11 cited

KPC: Learning-Based Model Predictive Control with Deterministic Guarantees

Emilio T. Maddalena, Paul Scharnhorst, Yuning Jiang +1

We propose Kernel Predictive Control (KPC), a learning-based predictive control strategy that enjoys deterministic guarantees of safety. Noise-corrupted samples of the unknown syst…

eess.SY2020

Deterministic error bounds for kernel-based learning techniques under bounded noise

Emilio T. Maddalena, Paul Scharnhorst, Colin N. Jones

We consider the problem of reconstructing a function from a finite set of noise-corrupted samples. Two kernel algorithms are analyzed, namely kernel ridge regression and $\varepsil…