Data fusion fault tolerant strategy for a quadrotor UAV under sensors and software faults

Hamadi, Hussein; Lussier, Benjamin; Fantoni, Isabelle; Francis, Clovis

doi:10.1016/j.isatra.2022.01.007

Cited by 25 publications

(5 citation statements)

References 35 publications

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“…An interesting study was conducted by Hamadi et al in [97]. The authors investigated the fault tolerant strategy in the event of both hardware sensor faults and software faults occurring in the quadrotor.…”

Section: Model-based Methodsmentioning

confidence: 99%

UAV Fault Detection Methods, State-of-the-Art

Puchalski

Giernacki

2022

Drones

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The continual expansion of the range of applications for unmanned aerial vehicles (UAVs) is resulting in the development of more and more sophisticated systems. The greater the complexity of the UAV, the greater the likelihood that a component will fail. Due to the fact that drones often operate in close proximity to humans, the reliability of flying robots, which directly affects the level of safety, is becoming more important. This review article presents recent research works on fault detection on unmanned flying systems. They include papers published between January 2016 and August 2022. Web of Science and Google Scholar databases were used to search for articles. Terminology related to fault detection of unmanned aerial vehicles was used as keywords. The articles were analyzed, each paper was briefly summarized and the most important details concerning each of the described articles were summarized in the table.

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Section: Model-based Methodsmentioning

confidence: 99%

UAV Fault Detection Methods, State-of-the-Art

Puchalski

Giernacki

2022

Drones

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“…As shown in Table 1, in this paper, the attribute of double-carbon data is set to m and the number of operations is n, and the attribute weight of data set J after the I-th data fusion is represented by Y 11 . (Y ma ,Y mb ) is expressed as the attribute value range [7][8][9] of each attribute of the data set after n data cleaning operations. When the attribute value is text data, it is transformed into numerical value through unicode encoding, thus calculating the attribute value domain.…”

Section: Select the Comprehensive Attribute Weights Of Dual-carbon Da...mentioning

confidence: 99%

Dual-carbon data fusion cleaning model based on Kafka cluster

Li,

Jiang,

Zhang

et al. 2024

Second International Conference on Informatics, Networking, and Computing (ICINC 2023)

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The conventional cleaning model of dual-carbon data fusion is more inclined to data cleaning, and there are mistakes in attribute judgment during data fusion, which leads to the problems of missing and duplication of the output cleaning data, which affects the application of dual-carbon data. Therefore, this paper studies the dual-carbon data fusion cleaning model based on Kafka cluster. Select the comprehensive attribute weights of the dual-carbon data fusion cleaning model, fuse the data with similar characteristics according to the characteristics of the data attributes, and find out the missing values in the process of repeated cleaning, so as to avoid the data fusion deviation. Based on Kafka cluster, the regularization term of dual-carbon data fusion cleaning target is constructed to reduce the dimension of dual-carbon data, and a regularization term is constructed on the basis of data fusion cleaning target function to make the fused dualcarbon data more meaningful. The comparison experiment shows that the cleaning performance of the model is better and it can be applied to real life.

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“…Similarly, the entries of the process noise covariance matrix Q i k are chosen. Once the parameters of the EKF i are set, the standard EKF algorithm [20] is used to obtain the required sensor fusion.…”

Section: Sensor Fusionmentioning

confidence: 99%

Sensor Fusion Architecture for Fault Diagnosis with a Predefined-Time Observer

Begovich,

Lizárraga,

Ramírez-Treviño

2024

Algorithms

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This study focuses on generating reliable signals from measured noisy signals through an enhanced sensor fusion method. The main contribution of this research is the development of a novel sensor fusion architecture that creates virtual sensors, improving the system’s redundancy. This architecture utilizes an input observer to estimate the system input, then it is introduced to the system model, the output of which is the virtual sensor. Then, this virtual sensor includes two filtering stages, both derived from the system’s dynamics—the input observer and the system model—which effectively diminish noise in the virtual sensors. Afterwards, the same architecture includes a classical sensor fusion scheme and a voter to merge the virtual sensors with the real measured signals, enhancing the signal reliability. The effectiveness of this method is shown by applying merged signals to two distinct diagnosers: one utilizes a high-order sliding mode observer, while the other employs an innovative extension of a predefined-time observer. The findings indicate that the proposed architecture improves diagnostic results. Moreover, a three-wheeled omnidirectional mobile robot equipped with noisy sensors serves as a case study, confirming the approach’s efficacy in an actual noisy setting and highlighting its principal characteristics. Importantly, the diagnostic systems can manage several simultaneous actuator faults.

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Data fusion fault tolerant strategy for a quadrotor UAV under sensors and software faults

Cited by 25 publications

References 35 publications

UAV Fault Detection Methods, State-of-the-Art

UAV Fault Detection Methods, State-of-the-Art

Dual-carbon data fusion cleaning model based on Kafka cluster

Sensor Fusion Architecture for Fault Diagnosis with a Predefined-Time Observer

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