DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Donghwan | ko |
dc.contributor.author | Kim, Do Wan | ko |
dc.date.accessioned | 2022-10-27T13:00:11Z | - |
dc.date.available | 2022-10-27T13:00:11Z | - |
dc.date.created | 2022-09-27 | - |
dc.date.created | 2022-09-27 | - |
dc.date.created | 2022-09-27 | - |
dc.date.issued | 2022-06-28 | - |
dc.identifier.citation | 17th IEEE International Conference on Control and Automation, ICCA 2022, pp.207 - 212 | - |
dc.identifier.issn | 1948-3449 | - |
dc.identifier.uri | http://hdl.handle.net/10203/299151 | - |
dc.description.abstract | The goal of this paper is to investigate model-free data-driven control design strategies for unknown systems. In particular, we report new data-driven linear matrix inequalities (LMIs) and dynamic programming (DP) methods. Both continuous-time and discrete-time systems are considered. We consider data transition equations that include complete information on the system model using state-input trajectories. Instead of computing explicit system model, the data transition equations are used to construct data-dependent LMI and DP formulations. The proposed formulations provide additional insights in data-driven control designs. In addition, we regard the proposed methods as a complement rather than replacement of existing methods. | - |
dc.language | English | - |
dc.publisher | IEEE Computer Society | - |
dc.title | Formulations for Data-Driven Control Design and Reinforcement Learning | - |
dc.type | Conference | - |
dc.identifier.scopusid | 2-s2.0-85135845082 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 207 | - |
dc.citation.endingpage | 212 | - |
dc.citation.publicationname | 17th IEEE International Conference on Control and Automation, ICCA 2022 | - |
dc.identifier.conferencecountry | IT | - |
dc.identifier.conferencelocation | Naples | - |
dc.identifier.doi | 10.1109/ICCA54724.2022.9831901 | - |
dc.contributor.localauthor | Lee, Donghwan | - |
dc.contributor.nonIdAuthor | Kim, Do Wan | - |
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