Abstract
Controlling oscillation is vital for applications in which flexible systems are employed. Many existing control methods rely on knowledge of the system dynamics to mitigate unwanted vibration. However, model-free methods can also be employed to control vibration. One method for model-free control is reinforcement learning (RL). Although the RL agent does not require information about the system to learn a control policy, domain knowledge of dynamics and control can be used to augment the agent and aid in generating an effective control policy. This work analyzes the effectiveness of training RL controllers that operate in combination with conventional controllers. Agents were trained in simulation using a model of a small-scale double-pendulum crane. The effect of the conventional control component on training as well as sensitivity to modeling error are analyzed. Agent transferability is investigated by implementing the simulation-trained controllers on a physical small-scale double-pendulum crane.
| Original language | English |
|---|---|
| Title of host publication | 2023 American Control Conference, ACC 2023 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 788-793 |
| Number of pages | 6 |
| ISBN (Electronic) | 9798350328066 |
| DOIs | |
| State | Published - 2023 |
| Event | 2023 American Control Conference, ACC 2023 - San Diego, United States Duration: May 31 2023 → Jun 2 2023 |
Publication series
| Name | Proceedings of the American Control Conference |
|---|---|
| Volume | 2023-May |
| ISSN (Print) | 0743-1619 |
Conference
| Conference | 2023 American Control Conference, ACC 2023 |
|---|---|
| Country/Territory | United States |
| City | San Diego |
| Period | 05/31/23 → 06/2/23 |
Bibliographical note
Publisher Copyright:© 2023 American Automatic Control Council.
Funding
ACKNOWLEDGMENT This work was supported by the Louisiana Board of Regents Support Fund Fellowship. 1Mechanical Engineering Department, University of Louisiana at Lafayette, Lafayette, Louisiana 2Oak Ridge National Laboratory [email protected] This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http: //energy.gov/downloads/doe-public-access-plan).
| Funders | Funder number |
|---|---|
| U.S. Department of Energy | |
| Louisiana Board of Regents |