Fault estimation based on sliding mode observer for Takagi–Sugeno fuzzy systems with digital communication constraints

Feng, Xuexin; Wang, Youqing

doi:10.1016/j.jfranklin.2019.09.020

Cited by 33 publications

(26 citation statements)

References 12 publications

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“…First, the normal data y and the fault data set x are inputted into the GPCA model (11), where the autocorrelation matrices of the normal and fault data are respectively estimated as…”

Section: B Fault Reconstruction Strategymentioning

confidence: 99%

“…In modern industrial processes, fault detection and diagnosis [1]- [11] have become one of the most critical areas of research in process control over the past decades, and an essential element in the operation of modern engineering systems to avoid serious consequences and reduce the maintenance costs. Since the manufacturing processes often have a large number of measured variables, and the measured variables have a high correlation, dimensionality reduction techniques have been widely used for process data analysis and process improvements.…”

Section: Introductionmentioning

confidence: 99%

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Generalized Principal Component Analysis-Based Subspace Decomposition of Fault Deviations and Its Application to Fault Reconstruction

Kong

Feng

2020

IEEE Access

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In the present work, based on the generalized principal component analysis, we propose a new approach to decompose the subspace of fault deviations, which is used for reconstruction-based fault diagnosis through principal component analysis (PCA) monitoring system. The proposed method is advanced since it lightens the computational burden by eliminating the irrelavant information and simplifying the fault subspace. The fault effects are extracted through analyzing the generalized principal components of the normal operating data and the fault data. The significant fault deviations that cause the alarming monitoring statistic are calculated. This is achieved by designing a two-part feature decomposition procedure. In the first part, the normal operating subspace is extracted through analyzing the generalized principal components of both the historical normal data and fault data. The fault-free part of the data is eliminated by projecting the data into the normal operating subspace. In the second part, principal component analysis is performed on the remaining part of the data, where the largest fault deviation directions are decomposed in order. By the two-part decomposition, an integrated fault subspace for all monitoring statistic indices is obtained, which separates the measurement data into two different parts for fault reconstruction. One part is related to the normal operating subspace, which is deemed to follow normal rules, and thus insignificant to remove alarming monitoring statistics. The other is related to the fault subspace, which contributes to the outof-control signals. Theoretical support is constructed and the related statistical characteristics are analyzed. Its feasibility and performance are illustrated with the data from the Tennessee Eastman (TE) benchmark process.

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“…First, the normal data y and the fault data set x are inputted into the GPCA model (11), where the autocorrelation matrices of the normal and fault data are respectively estimated as…”

Section: B Fault Reconstruction Strategymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Generalized Principal Component Analysis-Based Subspace Decomposition of Fault Deviations and Its Application to Fault Reconstruction

Kong

Feng

2020

IEEE Access

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“…erefore, in existing literature, fault estimation is a prerequisite for fault-tolerant control and has achieved affluence of theoretical research results [21][22][23]. At present, the methods of fault estimation mainly include observer-based methods [24,25], signal reconstruction-based methods [26,27], and artificial intelligence-based methods [28,29].…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of Novel LMI‐Based Iterative Learning Robust Nonlinear Controller

et al. 2021

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An iterative learning robust fault-tolerant control algorithm is proposed for a class of uncertain discrete systems with repeated action with nonlinear and actuator faults. First, by defining an actuator fault coefficient matrix, we convert the iterative learning control system into an equivalent unknown nonlinear repetitive process model. Then, based on the mixed Lyapunov function approach, we describe the stability of the nonlinear repetitive mechanism on time and trial indices and have appropriate conditions for the repeated control system’s stability in terms of linear matrix inequality theory. Through LMI techniques, we have obtained satisfactory results and controller stability, and robustness against fault tolerance is also discussed in detail. Finally, the simulation results of the output tracking control of the two exemplary models verify the effectiveness of the proposed algorithm.

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“…The estimation of states and unknown inputs (UIs) is important in fault diagnosis and dynamic system control. [1][2][3][4][5] This problem is frequently encountered in power system exciters, [6,7] chemical processes, [8][9][10] state estimation of a battery, [11][12][13] navigation, [14,15] and earthquake damage estimation. [16] Over the last decade, many methods have been proposed for the simultaneous estimation of the UIs and states in a linear discrete-time system, [17][18][19][20][21][22] among which Gillijns and De Moor [22] present a valuable overview.…”

Section: Introductionmentioning

confidence: 99%

“…The estimation of states and unknown inputs (UIs) is important in fault diagnosis and dynamic system control. [ 1–5 ] This problem is frequently encountered in power system exciters, [ 6,7 ] chemical processes, [ 8–10 ] state estimation of a battery, [ 11–13 ] navigation, [ 14,15 ] and earthquake damage estimation. [ 16 ]…”

Section: Introductionmentioning

confidence: 99%

Distributed semi‐cooperative filter for a nonlinear multi‐agent system with heterogeneous and homologous unknown inputs

Liu

et al. 2020

Can J Chem Eng

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In this study, the simultaneous estimation of the states and unknown inputs for a nonlinear multi-agent system with homologous and heterogeneous unknown inputs is performed. The decentralized sub-filter is used to estimate the states and heterogeneous unknown inputs, whereas the distributed subfilter is used to estimate the homologous unknown inputs. The extended Kalman filter is used to solve the estimation problem for nonlinear systems. Compared with previous studies, the distributed solution is improved to relax the existence of the homologous unknown input sub-filter. Moreover, the updating method of the residual generator is improved to relax the heterogeneous unknown input sub-filter. The practical problem of estimating the state of charge and temperature of the battery pack is used to verify the effectiveness of the proposed filter.

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Fault estimation based on sliding mode observer for Takagi–Sugeno fuzzy systems with digital communication constraints

Cited by 33 publications

References 12 publications

Generalized Principal Component Analysis-Based Subspace Decomposition of Fault Deviations and Its Application to Fault Reconstruction

Generalized Principal Component Analysis-Based Subspace Decomposition of Fault Deviations and Its Application to Fault Reconstruction

Design and Implementation of Novel LMI‐Based Iterative Learning Robust Nonlinear Controller

Distributed semi‐cooperative filter for a nonlinear multi‐agent system with heterogeneous and homologous unknown inputs

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