Active Fault Tolerant Control for uncertain neutral time delay system

Benjemaa, Rabeb; Elhsoumi, Aicha; Naoui, Saloua Bel Hadj Ali

doi:10.1109/ssd49366.2020.9364228

Cited by 10 publications

(3 citation statements)

References 14 publications

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“…For the trained model, to verify the effectiveness of the training, twenty-eight groups of signals were used for verification in this paper, and the seven types of signals, namely, normal, bias, blocking, drift, multiplicative, periodic, and internal fault, are labeled as the corresponding numbers [ 1 , 2 , 3 , 4 , 5 , 6 , 7 ]. Finally, the prediction diagram of the fault diagnosis platform obtained is shown in Figure 10 , where the blue dots indicate the actual signal type, and the black lines indicate the recognized signal type.…”

Section: Simulation and Verification Of The Platform Modelmentioning

confidence: 99%

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A New Dual-Mass MEMS Gyroscope Fault Diagnosis Platform

Cui

Zhang

et al. 2023

Micromachines

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MEMS gyroscopes are one of the core components of inertial navigation systems. The maintenance of high reliability is critical for ensuring the stable operation of the gyroscope. Considering the production cost of gyroscopes and the inconvenience of obtaining a fault dataset, in this study, a self-feedback development framework is proposed, in which a dualmass MEMS gyroscope fault diagnosis platform is designed based on MATLAB/Simulink simulation, data feature extraction, and classification prediction algorithm and real data feedback verification. The platform integrates the dualmass MEMS gyroscope Simulink structure model and the measurement and control system, and reserves various algorithm interfaces for users to independently program, which can effectively identify and classify seven kinds of signals of the gyroscope: normal, bias, blocking, drift, multiplicity, cycle and internal fault. After feature extraction, six algorithms, ELM, SVM, KNN, NB, NN, and DTA, were respectively used for classification prediction. The ELM and SVM algorithms had the best effect, and the accuracy of the test set was up to 92.86%. Finally, the ELM algorithm is used to verify the actual drift fault dataset, and all of them are successfully identified.

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Section: Simulation and Verification Of The Platform Modelmentioning

confidence: 99%

“…A fault diagnosis system needs to be able to perform three tasks: fault detection, to determine whether a fault has occurred in the system; fault isolation, to locate the fault and determine which sensor or actuator in the system has failed; and fault identification, to estimate the size, type or characteristics of the fault [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

A New Dual-Mass MEMS Gyroscope Fault Diagnosis Platform

Cui

Zhang

et al. 2023

Micromachines

View full text Add to dashboard Cite

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“…In recent years, there has been a burst of research activities in the area of time-delay control systems [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Active Fault Tolerant Control of Faulty Uncertain Neutral Time-Delay Systems

Benjemaa

Elhsoumi²,

Saloua³

2021

JAIBD

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The present paper attempts to investigate the problem of Fault Tolerant Control for a class of uncertain neutral time delay systems. In the first time, we consider an additive control that is based on adding a term to the nominal law when the fault occurs. This approach will be designed in three steps. The first step is fault detection while the second one is fault estimation. For these two steps, we consider the adaptive observer to guarantee the detection and estimation of the fault. The third step is the fault compensation. Lyapunov method and Linear Matrix Inequality (LMI) techniques were considered to improve the main method. Second, we propose a Pseudo Inverse Method "PIM" and determine the error between the closed loop and the nominal system. Finally, simulation results are presented to prove the theoretical development for an example of an uncertain neutral time delay system.

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