Facilitating autonomous systems with AI-based fault tolerance and computational resource economy

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dc.contributor.authorDeliparaschos, Kyriakos M.
dc.contributor.authorMichail, Konstantinos
dc.contributor.authorZolotas, Argyrios C.
dc.date.accessioned2020-05-13T09:12:57Z
dc.date.available2020-05-13T09:12:57Z
dc.date.issued2020-05-11
dc.description.abstractProposed is the facilitation of fault-tolerant capability in autonomous systems with particular consideration of low computational complexity and system interface devices (sensor/actuator) performance. Traditionally model-based fault-tolerant/detection units for multiple sensor faults in automation require a bank of estimators, normally Kalman-based ones. An AI-based control framework enabling low computational power fault tolerance is presented. Contrary to the bank-of-estimators approach, the proposed framework exhibits a single unit for multiple actuator/sensor fault detection. The efficacy of the proposed scheme is shown via rigorous analysis for several sensor fault scenarios for an electro-magnetic suspension testbed.en_UK
dc.identifier.citationDeliparaschos KM, Michail K, Zolotas AC. (2020) Facilitating autonomous systems with AI-based fault tolerance and computational resource economy. Electronics, Volume 9, Issue 5, May 2020, Article number 788en_UK
dc.identifier.issn0013-5070
dc.identifier.urihttps://doi.org/10.3390/electronics9050788
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/15448
dc.language.isoenen_UK
dc.publisherMDPIen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectartificial intelligenceen_UK
dc.subjectneural networksen_UK
dc.subjectMagleven_UK
dc.subjectreconfigurable controlen_UK
dc.subjectfault toleranceen_UK
dc.titleFacilitating autonomous systems with AI-based fault tolerance and computational resource economyen_UK
dc.typeArticleen_UK

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