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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 / M.S. / We use reinforcement learning to find two-point boundary optimum controls for linear time-varying systems with uncertain model dynamics. We divided the LTV control problem into two LTI subproblems using singular perturbation theory techniques. As a result, it is possible to identify the controller gains via a learning technique. We show that the training-based controller’s performance approaches that of the model-based optimal controller, with approximation accuracy growing with the temporal horizon of the control issue. In addition, we provide a simulated scenario to back up our findings.

Identiferoai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/115915
Date January 2023
CreatorsBaddam, Vasanth Reddy
ContributorsComputer Science and Applications, Eldardiry, Hoda, Watson, Layne T., Boker, Almuatazbellah (Muataz)
PublisherVirginia Tech
Source SetsVirginia Tech Theses and Dissertation
LanguageEnglish
Detected LanguageEnglish
TypeThesis
FormatETD, application/pdf, application/pdf
RightsCreative Commons Attribution 4.0 International, http://creativecommons.org/licenses/by/4.0/

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