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20202024
most citedPhysics-informed linear regression is competitive with two Machine Learning methods in residential building MPC

105 citations · 116 across the 8 of their papers we have counts for

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Showing 2023Show all

6 papers · 1 filter

eess.SY2023★ 1 cited

MIMO Grid Impedance Identification of Three-Phase Power Systems: Parametric vs. Nonparametric Approaches

Verena Häberle, Linbin Huang, Xiuqiang He +3

A fast and accurate grid impedance measurement of three-phase power systems is crucial for online assessment of power system stability and adaptive control of grid-connected conver…

eess.SY2023

Distributed Multi-Horizon Model Predictive Control for Network of Energy Hubs

Varsha Behrunani, Hanmin Cai, Philipp Heer +2

The increasing penetration of renewable energy resources has transformed the energy system from traditional hierarchical energy delivery paradigm to a distributed structure. Such d…

math.OC2023

A Dual System-Level Parameterization for Identification from Closed-Loop Data

Amber Srivastava, Mingzhou Yin, Andrea Iannelli +1

This work presents a dual system-level parameterization (D-SLP) method for closed-loop system identification. The recent system-level synthesis framework parameterizes all stabiliz…

eess.SY2023

Once upon a time step: A closed-loop approach to robust MPC design

Anilkumar Parsi, Marcell Bartos, Amber Srivastava +2

A novel perspective on the design of robust model predictive control (MPC) methods is presented, whereby closed-loop constraint satisfaction is ensured using recursive feasibility…

eess.SY2023

Error Bounds for Kernel-Based Linear System Identification with Unknown Hyperparameters

Mingzhou Yin, Roy S. Smith

The kernel-based method has been successfully applied in linear system identification using stable kernel designs. From a Gaussian process perspective, it automatically provides pr…

eess.SY2023

Diagonally Square Root Integrable Kernels in System Identification

Mohammad Khosravi, Roy S. Smith

In recent years, the reproducing kernel Hilbert space (RKHS) theory has played a crucial role in linear system identification. The core of a RKHS is the associated kernel character…