Facilitating autonomous systems with AI-based fault tolerance and computational resource economy - Deliparaschos, Kyriakos M.; Michail, Konstantinos; Zolotas, Argyrios (MDPI, 2020)
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.
Keywords: artificial intelligence; neural networks; Maglev; reconfigurable control; fault tolerance