Robust control of linear systems: a min-max reinforcement learning formulation

Simple item page
dc.contributor.authorFlores Campos, Juan Alejandro
dc.contributor.authorPerrusquía, Adolfo
dc.date.accessioned2024-01-04T16:34:57Z
dc.date.available2024-01-04T16:34:57Z
dc.date.issued2023-12-05
dc.description.abstractIn this paper, an online robust controller based on a min-max reinforcement learning approach for linear systems is discussed. Disturbances are represented by external signals coupled with the control input which are assumed to be bounded within a set of admissible disturbances. The proposed controller implements a min-max approach which realizes a smooth transition between optimal and robust controllers. Lyapunov stability theory is used to assess the stability and boundedness of the min-max robust formulation. A neural reinforcement learning architecture is used to obtain an approximation of the parameters associated to the optimal cost. Simulations are carried out to validate the proposed approach.en_UK
dc.identifier.citationFlores-Campos JA, Perrusquía A. (2023) Robust control of linear systems: a min-max reinforcement learning formulation. In: 2023 20th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE), 25-27 October 2023, Mexico City, Mexicoen_UK
dc.identifier.eisbn979-8-3503-0676-7
dc.identifier.eissn2642-3766
dc.identifier.isbn979-8-3503-0677-4
dc.identifier.issn2642-3774
dc.identifier.urihttps://doi.org/10.1109/CCE60043.2023.10332826
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/20610
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.titleRobust control of linear systems: a min-max reinforcement learning formulationen_UK
dc.typeConference paperen_UK

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Robust_control_of_linear_systems-2023.pdf
Size:
341.57 KB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.63 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Staff publications (SATM)