Sliding mode learning algorithm based adaptive neural observer strategy for fault estimation, detection and neural controller of an aircraft

Taimoor, Muhammad; Li, Aijun; Samiuddin, Muhammad

doi:10.1007/s12652-020-02390-4

Cited by 26 publications

(9 citation statements)

References 28 publications

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“…For which H ∞ technique is used for the purpose of stability as well as the consistency, theory of bi-index is used for the designing of FTC system. Many other algorithms and methodologies such as Kalman Filter SMO (Zhang et al , 2016; Djeghali et al , 2016), NNs (Chen et al , 2016; Taimoor and Aijun, 2019; Allen et al , 2016; Baghernezhad & horasani, 2016; Giorgi De et al , 2019; Fentaye et al , 2018; Yildirim and Kurt, 2019; Jia, and Duan, 2017; Amin et al , 2019; Amin et al , 2016; Taimoor and Aijun, 2020; Taimoor et al , 2020) and fuzzy logic (Ballesteros-Moncada et al , 2015) are implemented for the estimation of nonlinear parameters. In the above-mentioned techniques, NN techniques are better for faults identification because of the properties such as nonlinear function estimation property and learning abilities.…”

Section: Introductionmentioning

confidence: 99%

Adaptive rapid neural observer-based sensors fault diagnosis and reconstruction of quadrotor unmanned aerial vehicle

Taimoor

Maqsood³

et al. 2021

AEAT

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Purpose The objective of this research is to investigate various neural network (NN) observer techniques for sensors fault identification and diagnosis of nonlinear system in consideration of numerous faults, failures, uncertainties and disturbances. For the importunity of increasing the faults diagnosis and reconstruction preciseness, a new technique is used for modifying the weight parameters of NNs without enhancement of computational complexities. Design/methodology/approach Various techniques such as adaptive radial basis functions (ARBF), conventional radial basis functions, adaptive multi-layer perceptron, conventional multi-layer perceptron and extended state observer are presented. For increasing the fault detection preciseness, a new technique is used for updating the weight parameters of radial basis functions and multi-layer perceptron (MLP) without enhancement of computational complexities. Lyapunov stability theory and sliding-mode surface concepts are used for the weight-updating parameters. Based on the combination of these two concepts, the weight parameters of NNs are updated adaptively. The key purpose of utilization of adaptive weight is to enhance the detection of faults with high accuracy. Because of the online adaptation, the ARBF can detect various kinds of faults and failures such as simultaneous, incipient, intermittent and abrupt faults effectively. Results depict that the suggested algorithm (ARBF) demonstrates more confrontation to unknown disturbances, faults and system dynamics compared with other investigated techniques and techniques used in the literature. The proposed algorithms are investigated by the utilization of quadrotor unmanned aerial vehicle dynamics, which authenticate the efficiency of the suggested algorithm. Findings The proposed Lyapunov function theory and sliding-mode surface-based strategy are studied, which shows more efficiency to unknown faults, failures, uncertainties and disturbances compared with conventional approaches as well as techniques used in the literature. Practical implications For improvement of the system safety and for avoiding failure and damage, the rapid fault detection and isolation has a great significance; the proposed approaches in this research work guarantee the detection and reconstruction of unknown faults, which has a great significance for practical life. Originality/value In this research, two strategies such Lyapunov function theory and sliding-mode surface concept are used in combination for tuning the weight parameters of NNs adaptively. The main purpose of these strategies is the fault diagnosis and reconstruction with high accuracy in terms of shape as well as the magnitude of unknown faults. Results depict that the proposed strategy is more effective compared with techniques used in the literature.

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Section: Introductionmentioning

confidence: 99%

Adaptive rapid neural observer-based sensors fault diagnosis and reconstruction of quadrotor unmanned aerial vehicle

Taimoor

Maqsood³

et al. 2021

AEAT

Self Cite

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“…As such, different hardware-based, model-based, and data-driven approaches have been proposed in the literature [8][9][10][11][12][13][14][15][16][17][18]. A conventional class of FDI in the literature is called hardware redundancy techniques which employ multiple identical components for monitoring and acquiring data of interest and validation in a system [19][20][21]. However, the main disadvantage of this approach is imposing cost, weight, and complexity on the system.…”

Section: Introductionmentioning

confidence: 99%

“…However, the main disadvantage of this approach is imposing cost, weight, and complexity on the system. Moreover, the redundant hardware is usually used as a backup system at the occurrence of the fault and it is not able to provide any information of fault features such as fault time, fault shape, and its amplitude [10,11,20]. The second class of FDI approaches is called model-based techniques (analytical redundancy), which is established on the mathematical model of the underlying system.…”

Section: Introductionmentioning

confidence: 99%

Rapid Detection of Small Faults and Oscillations in Synchronous Generator Systems Using GMDH Neural Networks and High-Gain Observers

et al. 2021

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This paper presents a robust and efficient fault detection and diagnosis framework for handling small faults and oscillations in synchronous generator (SG) systems. The proposed framework utilizes the Brunovsky form representation of nonlinear systems to mathematically formulate the fault detection problem. A differential flatness model of SG systems is provided to meet the conditions of the Brunovsky form representation. A combination of high-gain observer and group method of data handling neural network is employed to estimate the trajectory of the system and to learn/approximate the fault- and uncertainty-associated functions. The fault detection mechanism is developed based on the output residual generation and monitoring so that any unfavorable oscillation and/or fault occurrence can be detected rapidly. Accordingly, an average L1-norm criterion is proposed for rapid decision making in faulty situations. The performance of the proposed framework is investigated for two benchmark scenarios which are actuation fault and fault impact on system dynamics. The simulation results demonstrate the capacity and effectiveness of the proposed solution for rapid fault detection and diagnosis in SG systems in practice, and thus enhancing service maintenance, protection, and life cycle of SGs.

show abstract

Section: Introductionmentioning

confidence: 99%

“…However, the main disadvantage of this approach is imposing cost, weight, and complexity on the system. Moreover, the redundant hardware is usually used as a backup system at the occurrence of the fault and it is not able to provide any information of fault features such as fault time, fault shape, and its amplitude [10,11,20]. The second class of FDI approaches is called model-based techniques (analytical redundancy), which is established upon the mathematical model of the underlying system.…”

Section: Introductionmentioning

confidence: 99%

Rapid Detection of Small Faults and Oscillations in Synchronous Generator Systems Using GMDH Neural Networks and High-Gain Observers

Ghanooni

Habibi

Yazdani

et al. 2021

Preprint

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This paper presents a robust and efficient fault detection and diagnosis framework for handling small faults and oscillations in synchronous generator (SG) systems. The proposed framework utilizes the Brunovsky form representation of nonlinear systems to mathematically formulate the fault detection problem. A differential-flatness model of SG systems is provided to meet the conditions of the Brunovsky form representation. A combination of high-gain observer and group method of data handling neural network is employed to estimate the trajectory of the system and to learn/ approximate the fault and uncertainties associated functions. The fault detection mechanism is developed based on output residual generation and monitoring so that any unfavourable oscillation and/or fault occurrence can be detected rapidly. Accordingly, an average L1-norm criterion is proposed for rapid decision making of faulty situations. The performance of the proposed framework is investigated for two benchmark scenarios which are actuation fault and fault impact on system dynamics. The simulation results demonstrate the capacity and effectiveness of the proposed solution for rapid fault detection and diagnosis in SG systems in practice, and thus enhancing service maintenance, protection, and life cycle of SGs.

show abstract

Sliding mode learning algorithm based adaptive neural observer strategy for fault estimation, detection and neural controller of an aircraft

Cited by 26 publications

References 28 publications

Adaptive rapid neural observer-based sensors fault diagnosis and reconstruction of quadrotor unmanned aerial vehicle

Adaptive rapid neural observer-based sensors fault diagnosis and reconstruction of quadrotor unmanned aerial vehicle

Rapid Detection of Small Faults and Oscillations in Synchronous Generator Systems Using GMDH Neural Networks and High-Gain Observers

Rapid Detection of Small Faults and Oscillations in Synchronous Generator Systems Using GMDH Neural Networks and High-Gain Observers

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