Safety control system technologies for UAVs: review and prospect

Guo, Lei; Yu, Xiang; Zhang, Xiao; Zhang, Youmin

doi:10.1360/ssi-2019-0101

Cited by 11 publications

(2 citation statements)

References 42 publications

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“…The authors of [47] present a fairly broad approach to the subject of multisource interference, faults and failures described in the studies from 1976 to 2019. They present the characteristics of various disorders, their estimation and detection, as well as methods of counteracting and minimizing the effects of this type of irregularities, including mission reconfiguration techniques.…”

Section: Existing Surveys and Reviewsmentioning

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: Existing Surveys and Reviewsmentioning

confidence: 99%

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

Puchalski

Giernacki

2022

Drones

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“…The control system of the owner aircraft (that is flying in a specific route) updates its subsequent trajectory with respect to the built-in regression model, while sensory radar information is being provided simultaneously [23]. Finally, the safety control system takes proper actions in order to avoid a possible collision [24].…”

Section: Related Work On Trajectory-based Operationsmentioning

confidence: 99%

New Reliability Studies of Data-Driven Aircraft Trajectory Prediction

Hashemi¹,

Botez²,

Grigorie

2020

Aerospace

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Two main factors, including regression accuracy and adversarial attack robustness, of six trajectory prediction models are measured in this paper using the traffic flow management system (TFMS) public dataset of fixed-wing aircraft trajectories in a specific route provided by the Federal Aviation Administration. Six data-driven regressors with their desired architectures, from basic conventional to advanced deep learning, are explored in terms of the accuracy and reliability of their predicted trajectories. The main contribution of the paper is that the existence of adversarial samples was characterized for an aircraft trajectory problem, which is recast as a regression task in this paper. In other words, although data-driven algorithms are currently the best regressors, it is shown that they can be attacked by adversarial samples. Adversarial samples are similar to training samples; however, they can cause finely trained regressors to make incorrect predictions, which poses a security concern for learning-based trajectory prediction algorithms. It is shown that although deep-learning-based algorithms (e.g., long short-term memory (LSTM)) have higher regression accuracy with respect to conventional classifiers (e.g., support vector regression (SVR)), they are more sensitive to crafted states, which can be carefully manipulated even to redirect their predicted states towards incorrect states. This fact poses a real security issue for aircraft as adversarial attacks can result in intentional and purposely designed collisions of built-in systems that can include any type of learning-based trajectory predictor.

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A Composite Adaptive Fault-Tolerant Attitude Control for a Quadrotor UAV with Multiple Uncertainties

Wang

Zhang

2022

J Syst Sci Complex

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Safety control system technologies for UAVs: review and prospect

Cited by 11 publications

References 42 publications

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

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

New Reliability Studies of Data-Driven Aircraft Trajectory Prediction

A Composite Adaptive Fault-Tolerant Attitude Control for a Quadrotor UAV with Multiple Uncertainties

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