Robust Three Stage Central Difference Kalman Filter for Helicopter Unmanned Aerial Vehicle Actuators Fault Estimation

Nejati, Zahra; Faraji, Alireza; Abedi, Mostafa

doi:10.5829/ije.2021.34.05b.23

Cited by 2 publications

(3 citation statements)

References 16 publications

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“…They demonstrated the effectiveness of the proposed solution for various faults in actuators and simultaneous faults for helicopter actuators with external disturbance. The described method was also used in a later study [93].…”

Section: Model-based Methodsmentioning

confidence: 99%

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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%

“…In another work [93], Nejati et al consider robust three-stage central difference Kalman filter for UAV actuators fault in the presence of external disturbance. The filter used is an extension of the solution proposed by Zhong et al in [74].…”

Section: Model-based Methodsmentioning

confidence: 99%

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

Puchalski

Giernacki

2022

Drones

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“…For implementing this filter to SG's dynamic, a complete description of the states and measurements is required. Equations 1 and 2 represent the usual form of the states and measurement, Also, special attention should be paid to the discontinuity and continuity of these equations with respect to time, which will be explained more (18,19).…”

Section: Central Difference Kalman Filtermentioning

confidence: 99%

Synchronous Generator Dual Estimation Using Sigma Points Kalman Filter

Zoghi,

Yaghobi

2024

IJE

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In this article, the central difference Kalman filter (CDKF) has been used to estimate the parameters of two different models of synchronous generator (SG) in the presence of noise. It should be mentioned that there are different models of synchronous generators with different levels of accuracy for use in estimation algorithms. The estimation algorithm in this paper uses a smaller number of measurement inputs to estimate the states and unknown parameters for two exact models of the synchronous generator. The central difference Kalman filter (CDKF) is a member of the Kalman filter family, which, like the unscented Kalman filter (UKF), uses sigma points to model nonlinear equations. The differential Kalman filter (CDKF) provides better results than the unscented Kalman filter. In this research, by using two synchronous generator models with different parameters in three scenarios, the ability of the Kalman filter of the central difference is challenged, which shows that this method is very efficient and reliable.

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Robust Three Stage Central Difference Kalman Filter for Helicopter Unmanned Aerial Vehicle Actuators Fault Estimation

Cited by 2 publications

References 16 publications

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

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

Synchronous Generator Dual Estimation Using Sigma Points Kalman Filter

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