A review of safe online learning for nonlinear control systems

Simple item page
dc.contributor.authorOsborne, Matthew
dc.contributor.authorShin, Hyosang
dc.contributor.authorTsourdos, Antonios
dc.date.accessioned2021-07-28T10:46:19Z
dc.date.available2021-07-28T10:46:19Z
dc.date.issued2021-07-19
dc.description.abstractLearning for autonomous dynamic control systems that can adapt to unforeseen environmental changes are of great interest but the realisation of a practical and safe online learning algorithm is incredibly challenging. This paper highlights some of the main approaches for safe online learning of stabilisable nonlinear control systems with a focus on safety certification for stability. We categorise a non-exhaustive list of salient techniques, with a focus on traditional control theory as opposed to reinforcement learning and approximate dynamic programming. This paper also aims to provide a simplified overview of techniques as an introduction to the field. It is the first paper to our knowledge that compares key attributes and advantages of each technique in one paper.en_UK
dc.identifier.citationOsborne M, Shin H-S, Tsourdos A. (2021) A review of safe online learning for nonlinear control systems. In: 2021 International Conference on Unmanned Aircraft Systems (ICUAS), 15-18 June 2021, Athensen_UK
dc.identifier.issn2575-7296
dc.identifier.urihttps://doi.org/10.1109/ICUAS51884.2021.9476765
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/16941
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectNonlinear dynamical systemsen_UK
dc.subjectReal-time systemsen_UK
dc.subjectNonlinear control systemsen_UK
dc.subjectReinforcement learningen_UK
dc.subjectHeuristic algorithmsen_UK
dc.titleA review of safe online learning for nonlinear control systemsen_UK
dc.typeConference paperen_UK

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Review_of_Safe_Online_Learning_for_Nonlinear-2021.pdf
Size:
2.87 MB
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)