An Analytical Robust Sensor Fault Detection and Fault Tolerant Control in Quadrotor

Mohammadi, Arman; Ramezani, Amin

doi:10.3103/s0146411621010053

Cited by 5 publications

(2 citation statements)

References 20 publications

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“…e detection performance of the system is comprehensively evaluated through SNR, FAR, and detection rate. e curve of these three indexes is called the Receiver Operation Curve (ROC) [30]. Here, the output test statistic z is used as a Gaussian variable, and the threshold k is used.…”

Section: Performance Test Of Fos Spsmentioning

confidence: 99%

Application of Smart Fiber Optic Sensor Technology in Feature Extraction, Recognition, and Detection

Gao

Cai

2022

Wireless Communications and Mobile Computing

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To improve the signal recognition effect of the security system, this paper studies the security system based on intelligent Fiber Optic Sensor (FOS) technology. Firstly, the research background of intelligent FOS is introduced, and its current situation in feature extraction, recognition, and detection is introduced. Secondly, the double Mach–Zehnder (M-Z) Optical Fiber- (OF-) based interferometer model is implemented, and the Wavelet Analysis (WA) theory is introduced to analyze the characteristic threshold and Frequency (F) characteristics of intrusion signal. Finally, the distributed intelligent FOS-based perimeter security system is constructed, and an empirical study is conducted to verify its performance. The results show that the intruder knocking-induced signal F, intruder climbing-induced signal F, noiseless environment-induced signal F, and rainy environment-induced signal F are 0–250 kHz, 0–25 kHz, 0–1.5 kHz, and 0–3.5 kHz, respectively; in all the four cases, excellent results have been obtained after wavelet threshold denoising. Meanwhile, the received signal is decomposed into seven layers through multiscale WA theory. The signal feature classification is based on WA and takes variance as the representation, and the clear classification results are obtained; when the False Alarm Rate (FAR) = 1%, the detection probability of the proposed system is about 99%, while the detection probability of the traditional system is about 3%. The reference arm and sensing arm of the distributed OF-based perimeter security system can be laid in the same optical cable. Therefore, the designed wavelet threshold filtering method is feasible, and the detection probability of the designed WA-based system is higher than that of the traditional security system. The research content provides a reference for the development of intelligent FOS technology in the field of security.

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Section: Performance Test Of Fos Spsmentioning

confidence: 99%

Application of Smart Fiber Optic Sensor Technology in Feature Extraction, Recognition, and Detection

Gao

Cai

2022

Wireless Communications and Mobile Computing

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“…In Bouabdallah et al (2004), a combined method is presented using the PID + linear–quadratic (LQ) controller in order to control the indoor drone. In Mohammadi et al (2021), which is the previous work of the authors, a PID-based FTC is presented to tackle the effect of fault which has also acceptable robustness against uncertainty and fault. The comparison between the proposed method with this former method and the other PID-based studies is represented in this paper which reports the better performance of proposed model predictive control (MPC) method.…”

Section: Introductionmentioning

confidence: 99%

A robust model predictive control-based method for fault detection and fault tolerant control of quadrotor UAV

Mohammadi

Ramezani

2022

Transactions of the Institute of Measurement and Control

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In this study, first a model predictive control (MPC) solution is utilized to control the quadrotor model in both fault-free and faulty modes by employing a cost function. For this aim, the obtained non-linear continuous model has been discretized after linearization around an equilibrium point. Then, a fault detection and diagnosis (FDD) and fault tolerant control (FTC) strategy is proposed based on MPC in order to neutralize the effect of fault. The method of FDD and FTC designing is based on analytical redundancy relations (ARRs) which detects and tolerates the fault by producing a residual signal. Therefore, this method presents a complete FTC solution to fault issue without using observer. In addition, this solution has robustness against the uncertainties and disturbances which may challenge the model in practical situations. The discussed fault in this paper is a sensor fault which is modeled as an undesired function which affects the sensor of roll angle. However, the proposed FTC can tolerate all types of faults. The results of implementation of this method are validated under various simulation scenarios.

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Fault diagnosis method for redundancy sensors of electric actuator

CUI,

LI,

SUN

2024

JNWPU

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In order to solve the singular fault modes of redundant position sensors in electric actuators, a fault diagnosis method based on Hall sensor analytical model is proposed on the basis of traditional redundancy comparison algorithm. Through the complete self-monitoring design of Hall sensor and the analytical model design, non-similar redundancy signals of sensors are formed. At the same time, a hypothesis testing method based on sliding data window is adopted, and by adjusting the width of the sliding window to meet the indicators of false alarm rate and missed alarm rate, a fault diagnosis algorithm for position sensors is formed as a whole. Finally, an algorithm validation was conducted for the 2∶2 singular fault mode of the position sensor. The results showed that the fault diagnosis algorithm based on the Hall sensor model can effectively solve the problems caused by 2∶2 singular faults, greatly improve the availability of sensor signals, and ensure the control accuracy and fault tolerance of the servo closed-loop.

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An Analytical Robust Sensor Fault Detection and Fault Tolerant Control in Quadrotor

Cited by 5 publications

References 20 publications

Application of Smart Fiber Optic Sensor Technology in Feature Extraction, Recognition, and Detection

Application of Smart Fiber Optic Sensor Technology in Feature Extraction, Recognition, and Detection

A robust model predictive control-based method for fault detection and fault tolerant control of quadrotor UAV

Fault diagnosis method for redundancy sensors of electric actuator

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