Deliparaschos, Kyriakos M.Michail, KonstantinosZolotas, Argyrios C.2020-05-132020-05-132020-05-11Deliparaschos 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 7880013-5070https://doi.org/10.3390/electronics9050788https://dspace.lib.cranfield.ac.uk/handle/1826/15448Proposed 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.enAttribution 4.0 Internationalartificial intelligenceneural networksMaglevreconfigurable controlfault toleranceArticle