Distributed Control meets Machine Learning

Location of research: On site

We are sorry, this position has been filled.

This project will develop machine learning algorithms and theory to build data-driven dynamic models that are suitable for distributed control. In order to apply methods from distributed control, a system model needs to exhibit some sort of sparsity structure, typically when inferring models from data, incorporating structural constraints makes the problem intractable. In this project we will seek to design ML algorithms that impose structured model realizations. It is important that we are able to characterize the sample complexity and the model error.

This project is best suited to a student with a strong background in convex optimization and control theory.

Direct Supervisor: James Anderson

Name of lab: Anderson Group

Location of lab: North West Corner Building, 10th floor

Dates: 6/1/2021 - 8/20/2021

Hours per week: 25

Paid: Yes

Credit: No

Qualifications: Convex optimization, control theory

Eligibility: Master's, PhD 1st year (SEAS only)

James Anderson, [email protected]