Learning-Based Pareto Optimal Control of Large-Scale Systems with Unknown Slow Dynamics

We develop a data-driven approach to Pareto optimal control of large-scale systems, where decision makers know only their local dynamics. Using reinforcement learning, we design a control strategy that optimally balances multiple objectives. The proposed method achieves near-optimal performance and scales well with the total dimension of the system. Experimental results demonstrate the effectiveness of our approach in managing multi-area power systems.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Electrical Engineering

Department dc:contributor.department

Electrical Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2024

Author dc:creator

• Tajik Hesarkuchak, Saeed

Chair dc:contributor.committeechair

• Boker, Almuatazbellah M.

Committee members dc:contributor.committeemember

• Eldardiry, Hoda Mohamed
• Mili, Lamine M.

• Control
• Large scale systems
• Data Driven Control

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:40553

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/119384