Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/19336
Title: | Facilitating autonomous systems with AI-based fault tolerance and computational resource economy |
Authors: | Deliparaschos, Kyriakos M. Michail, Konstantinos Zolotas, Argyrios C. |
Major Field of Science: | Natural Sciences |
Field Category: | Computer and Information Sciences |
Keywords: | Artificial intelligence;Fault tolerance;Maglev;Neural networks;Reconfigurable control |
Issue Date: | May-2020 |
Source: | Electronics, 2020, vol. 9, no. 5, articl. no. 788 |
Volume: | 9 |
Issue: | 5 |
Journal: | Electronics |
Abstract: | Proposed 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. |
URI: | https://hdl.handle.net/20.500.14279/19336 |
ISSN: | 20799292 |
DOI: | 10.3390/electronics9050788 |
Rights: | © by the authors. Attribution-NonCommercial-NoDerivatives 4.0 International |
Type: | Article |
Affiliation : | Cyprus University of Technology Cranfield University SignalGeneriX Ltd |
Publication Type: | Peer Reviewed |
Appears in Collections: | Άρθρα/Articles |
Files in This Item:
File | Description | Size | Format | |
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electronics-09-00788-v2.pdf | Fulltext | 1.64 MB | Adobe PDF | View/Open |
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