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Dominic Liao-McPherson

Dr.
Dominic Liao-McPherson
Alumni
ETH Zurich
dliaomc@ethz.ch
Curriculum Vitae
I'm excited by the potential of ubiquitous automation to benefit society using control theory, machine learning, and optimization.

Dominic Liao-McPherson is a Postdoc in the Automatic Control Lab at ETH Zurich. He obtained the BASc degree in Engineering Science from the University of Toronto in 2015 and  his PhD in Aerospace Engineering and Scientific Computing from the University of Michigan (US) in 2020 where his research focused on suboptimality in model predictive control and applications in engine emissions reduction. His research interests lie in the intersection of systems theory, optimization, and numerical methods where he studies intelligent autonomous  systems and the algorithms that control them.

Scientific Publications

Published
Sequential Quadratic Programming-based Iterative Learning Control for Nonlinear Systems
ArXiv
Samuel Balula, Efe Balta, Dominic Liao-McPherson, Alisa Rupenyan, John Lygeros
Collection
Published
Stochastic Wasserstein Gradient Flows using Streaming Data with an Application in Predictive Maintenance
ArXiv
Nicolas Lanzetti, Efe Balta, Dominic Liao-McPherson, Florian Dörfler
Collection
Published
Drone-based Volume Estimation in Indoor Environments
IFAC World Congress 2023
Samuel Balula, Dominic Liao-McPherson, Stefan Stevsic, Alisa Rupenyan, John Lygeros
Collection
Published
In-layer Thermal Control of a Multi-layer Selective Laser Melting Process
2022 European Control Conference (ECC)
Pages 1678 - 1683
Dominic Liao-McPherson, Efe Balta, Ryan Wüest, Alisa Rupenyan, John Lygeros
Doi.org Collection
to Appear
Receding Horizon Games with Coupling Constraints for Demand-Side Management
61st IEEE Conference on Decision and Control (CDC 2022)
Sophie Hall, Giuseppe Belgioioso, Dominic Liao-McPherson, Florian Dörfler
Doi.org Collection
Published
On Robustness in Optimization-Based Constrained Iterative Learning Control
IEEE Control Systems Letters
Vol 6 Pages 2846-2851
Dominic Liao-McPherson, Efe Balta, Alisa Rupenyan, John Lygeros
Doi.org Collection
Published
Cross-layer design for real-time grid operation: Estimation, optimization and power flow
Electric Power Systems Research
Vol 212 No 108378
Miguel Picallo, Dominic Liao-McPherson, Saverio Bolognani, Florian Dörfler
Collection
Sampled-Data Online Feedback Equilibrium Seeking: Stability and Tracking
60th IEEE Conference on Decision and Control (CDC21)
Giuseppe Belgioioso, Dominic Liao-McPherson, Mathias Badyn, Saverio Bolognani, John Lygeros, Florian Dörfler
Collection

Research projects as Researcher

Title
Principal Investigators

Data-assisted model predictive control with applications to industrial processes

Alisa Rupenyan, John Lygeros

Summary

Manufacturing tasks are often repetitive, involving the same processing steps being repeated over and over to produce identical parts. This makes them well suited for data driven methods that exploit repetition, like the ones developed for autonomous racing. This project addresses deploying the data-assisted predictive control methods in manufacturing applications. The resulting optimisation problems are often of formidable complexity, making them challenging to solve in real time. This calls for efficient solution methods that inherit the stability and performance guarantees of the predictive control design even when the search for the optimal solution has to be terminated prematurely.

People involved

Alisa Rupenyan
Alisa Rupenyan, John Lygeros, Dominic Liao-McPherson
Data-assisted model predictive control with applications to industrial processes
Alisa Rupenyan, John Lygeros

Manufacturing tasks are often repetitive, involving the same processing steps being repeated over and over to produce identical parts. This makes them well suited for data driven methods that exploit repetition, like the ones developed for autonomous racing. This project addresses deploying the data-assisted predictive control methods in manufacturing applications. The resulting optimisation problems are often of formidable complexity, making them challenging to solve in real time. This calls for efficient solution methods that inherit the stability and performance guarantees of the predictive control design even when the search for the optimal solution has to be terminated prematurely.

Alisa Rupenyan
Alisa Rupenyan, John Lygeros, Dominic Liao-McPherson
147
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