Formulations for Data-Driven Control Design and Reinforcement Learning

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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.
Publisher
IEEE Computer Society
Issue Date
2022-06-28
Language
English
Citation

17th IEEE International Conference on Control and Automation, ICCA 2022, pp.207 - 212

ISSN
1948-3449
DOI
10.1109/ICCA54724.2022.9831901
URI
http://hdl.handle.net/10203/299151
Appears in Collection
EE-Conference Papers(학술회의논문)
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