Event-Based Dissipative Control of Interval Type-2 Fuzzy Markov Jump Systems Under Sensor Saturation and Actuator Nonlinearity

Ran, Guangtao; Li, Chuanjiang; Lam, Hak‐Keung; Li, Dongyu; Han, Chonghun

doi:10.1109/tfuzz.2020.3046335

Cited by 38 publications

(16 citation statements)

References 57 publications

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“…A simulation study showed that the combined JITL-PCA models outperformed PCA in the analyzing of nonlinear signals [26]. In addition, neural network methods and the stochastic hidden Markov model (HMM) were studied to improve FD performance of dynamic systems [29,30].…”

Section: Introductionmentioning

confidence: 99%

Fault Detection for High-Speed Trains Using CCA and Just-in-Time Learning

et al. 2022

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Online monitors of the running gears systems of high-speed trains play critical roles in ensuring operational safety and reliability. Status signals collected from high-speed train running gears are very complex regarding working environments, random noises and many other real-world constraints. This paper proposed fault detection (FD) models using canonical correlation analysis (CCA) and just-in-time learning (JITL) to process scalar signals of high-speed train gears, named as CCA-JITL. After data preprocessing and normalization, CCA transforms covariance matrices of high-dimension historical data into low-dimension subspaces and maximizes correlations between the most important latent dimensions. Then, JITL components formulate local FD models which utilize subsets of testing samples with larger Euclidean distances to training data. A case study introduced a novel system design of an online FD architecture and demonstrated that CCA-JITL FD models significantly outperformed traditional CCA models. The approach is applicable to other dimension reduction FD models such as PCA and PLS.

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Section: Introductionmentioning

confidence: 99%

Fault Detection for High-Speed Trains Using CCA and Just-in-Time Learning

et al. 2022

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“…When this issue is considered, the general T-S fuzzy modeling scheme cannot achieve the desired results [15]. The IT2 fuzzy model was developed because of its good proxy for nonlinear systems with parameter uncertainty [29][30][31][32][33][34][35][36][37]. The problem of the FD filtering method is proposed with event-based, which is the application in IT2 fuzzy theory under the framework of networked timedelay control systems [29].…”

Section: Introductionmentioning

confidence: 99%

Fault Detection for Interval Type-2 T-S Fuzzy Networked Systems via Event-Triggered Control

Zhang

Fei

et al. 2022

Machines

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This paper investigates the event-triggered fault diagnosis (FD) problem for interval type-2 (IT2) Takagi–Sugeno (T-S) fuzzy networked systems. Firstly, an FD fuzzy filter is proposed by using IT2 T-S fuzzy theory to generate a residual signal. This means that the FD filter premise variable needs to not be identical to the nonlinear networked systems (NNSs). The evaluation functions are referenced to determine the occurrence of system faults. Secondly, under the event-triggered mechanism, a fault residual system (FRS) is established with parameter uncertainty, external disturbance and time delay, which can reduce signal transmission and communication pressure. Thirdly, the progressive stability of the fault residual system is guaranteed by using the Lyapunov theory. For the energy bounded condition of external noise interference, the performance criterion is established using linear matrix inequalities. The matrix parameters of the target FD filter are obtained by the convex optimization method. A less conservative fault diagnosis method can be obtained. Finally, the simulation example is provided to illustrate the effectiveness and the practicalities of the proposed theoretical method.

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“…Combining a set of IF-THEN rules with some fuzzy sets, makes it possible to approximate nonlinear systems with high precision using a series of linear subsystems. As a result, recent years have seen an increase in research interest in the control issues associated with T-S fuzzy systems [2]- [6]. The authors in [5] have discussed the stability of a T-S fuzzy system with state quantization under exponential dissipation using a non-fragile sampled-data control.…”

Section: Introductionmentioning

confidence: 99%

Event-Triggered L ₂–L _∞ Filtering for Network-Based Neutral Systems With Time-Varying Delays via T-S Fuzzy Approach

et al. 2021

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This article examines the issue of event-triggered L 2 −L ∞ filtering for network-based neutral systems via Takagi-Sugeno (T-S) fuzzy approach. A dynamic discrete event-triggered scheme (ETS) is introduced to save the limited communication resource. Based on the T-S fuzzy model, the consider neutral type system with networked induced delays are represented as a class of T-S fuzzy system. In addition, by considering a novel Lyapunov-Krasovskii functional (LKF) and by using the Wirtinger inequality technique, the stability conditions with respect to linear matrix inequalities (LMIs) are presented to guarantee the considered filtering systems are asymptotically stable with L 2 − L ∞ performance index γ. To the end, numerical examples are given to illustrate the effectiveness of the proposed result.

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Event-Based Dissipative Control of Interval Type-2 Fuzzy Markov Jump Systems Under Sensor Saturation and Actuator Nonlinearity

Cited by 38 publications

References 57 publications

Fault Detection for High-Speed Trains Using CCA and Just-in-Time Learning

Fault Detection for High-Speed Trains Using CCA and Just-in-Time Learning

Fault Detection for Interval Type-2 T-S Fuzzy Networked Systems via Event-Triggered Control

Event-Triggered L ₂–L _∞ Filtering for Network-Based Neutral Systems With Time-Varying Delays via T-S Fuzzy Approach

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Event-Based Dissipative Control of Interval Type-2 Fuzzy Markov Jump Systems Under Sensor Saturation and Actuator Nonlinearity

Cited by 38 publications

References 57 publications

Fault Detection for High-Speed Trains Using CCA and Just-in-Time Learning

Fault Detection for High-Speed Trains Using CCA and Just-in-Time Learning

Fault Detection for Interval Type-2 T-S Fuzzy Networked Systems via Event-Triggered Control

Event-Triggered L 2–L ∞ Filtering for Network-Based Neutral Systems With Time-Varying Delays via T-S Fuzzy Approach

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Product

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Event-Triggered L ₂–L _∞ Filtering for Network-Based Neutral Systems With Time-Varying Delays via T-S Fuzzy Approach