Turbojet Engine Control of UAV using Artificial Neural Network PID

Kim, Dae-Gi; Hong, Gyo-Young; Ahn, Dong-Man; Hong, Seung-Beom; Jie, Min-Seok

doi:10.12673/jant.2014.18.2.107

Cited by 1 publication

(1 citation statement)

References 5 publications

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“…13,14 To reflect these features in the simulation, equations (7) to (9) about the propulsion force and moment were shown. 15…”

Section: Model and Control System Of The Unmanned Surface Vehiclementioning

confidence: 99%

A study on anomaly detection of unmanned marine systems using machine learning

Jeong

et al. 2022

Measurement and Control

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To operate unmanned systems in the ocean, many elements are required. They could be factors related to the equipment performance or operating methods. However, the aforementioned factors are suitable for situations not assuming unexpected events; in the actual ocean, unmanned equipment has the potential to experience problems in operation due to several factors such as environmental or external forces. Having problems in the propulsion system of unmanned marine equipment in unexpected situations affects the execution of duty of the unmanned equipment and connotes the risk of equipment damage and loss. Also, a lot of data generated in the real world is distributed disproportionately for each class, which means that the data of the minor class consists of significantly less data than the data of the majority class, and the normal data, in contrast, anomaly data is a very small amount of data, and there is very little data for learning. Therefore, in this study, anomaly detection based on unsupervised learning was conducted about these problems. In this study, we evaluated anomaly detection based on unsupervised learning to resolve these problems. Based on the results, the state of unmanned systems being operated in the ocean are identified and the anomaly state is examined. In this study, the auto-encoder (AE) method that learns the features of the normal area of the judgment data, that is, a part of machine learning, and the LSTM method for predicting time series states are utilized. This machine learning technology uses time-series learning data on the state of the unmanned system, creates standards to determine normal and anomaly states, and in turn, learns the normal state and determines the anomaly state. Furthermore, the simulation of the proposed method was carried out to verify the algorithm used in this study, and its validity was verified by comparing its data with the measured data. By applying the deduced anomaly detection information to the unmanned surface vehicle (USV) simulator, a fault tolerance control system was designed to enable the execution of duty even in the situation of an anomaly propeller of the USV, and the effectiveness of this study was verified.

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“…13,14 To reflect these features in the simulation, equations (7) to (9) about the propulsion force and moment were shown. 15…”

Section: Model and Control System Of The Unmanned Surface Vehiclementioning

confidence: 99%