Learning-based Optimal Control of Time-Varying Linear Systems Over Large Time Intervals

We solve the problem of two-point boundary optimal control of linear time-varying systems with unknown model dynamics using reinforcement learning. Leveraging singular perturbation theory techniques, we transform the time-varying optimal control problem into two time-invariant subproblems. This allows the utilization of an off-policy iteration method to learn the controller gains. We show that the performance of the learning-based controller approximates that of the model-based optimal controller and the approximation accuracy improves as the control problem’s time horizon increases. We also provide a simulation example to verify the results

Name thesis:degree_name

M.S.

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Computer Science

Department dc:contributor.department

Computer Science and Applications

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2023

Author dc:creator

• Baddam, Vasanth Reddy

Chairs dc:contributor.committeechair

• Eldardiry, Hoda
• Boker, Almuatazbellah (Muataz)

Committee member dc:contributor.committeemember

• Watson, Layne T.

• optimal control
• singular perturbation
• reinforcement learning

Statement dc:rights

• Creative Commons Attribution 4.0 International

Licence dc:rights.uri

• http://creativecommons.org/licenses/by/4.0/

Language dc:language.iso

en

Handle dc:identifier.uri

http://hdl.handle.net/10919/115915

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/115915