2020 28th Mediterranean Conference on Control and Automation (MED) 2020
DOI: 10.1109/med48518.2020.9183151
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A Fractional-Order Analytical Redundancy Approach for Fault Detection on a Hovering Helicopter

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“…Other approaches to fractional order fault detection have also been considered and studied, a notable example are the learning based approaches such as neural network Disturbance observers [40], single layer and double layer radial basis function neural networks (RBFNN) [41], and recurrent wavelet fuzzy neural networks (RWFNN) [42]. Other approaches comprise state observers for actuator faults [43] and sensor faults [44,45,46,47], Fractional observers [48], high gain observers [49] and Generalized fractional order observers [50] for robust fault isolation, adaptive non linear observers [51], Non-fragile H ∞ filter for fault detection [52,53], fractional disturbance observers [54].…”
Section: Fractional Order Fault Detection and Diagnosismentioning
confidence: 99%
“…Other approaches to fractional order fault detection have also been considered and studied, a notable example are the learning based approaches such as neural network Disturbance observers [40], single layer and double layer radial basis function neural networks (RBFNN) [41], and recurrent wavelet fuzzy neural networks (RWFNN) [42]. Other approaches comprise state observers for actuator faults [43] and sensor faults [44,45,46,47], Fractional observers [48], high gain observers [49] and Generalized fractional order observers [50] for robust fault isolation, adaptive non linear observers [51], Non-fragile H ∞ filter for fault detection [52,53], fractional disturbance observers [54].…”
Section: Fractional Order Fault Detection and Diagnosismentioning
confidence: 99%