State and Fault Estimations for Discrete-Time T-S Fuzzy Systems With Sensor and Actuator Faults

Mu, Yunfei; Zhang, Huaguang; Xi, Ruipeng; Gao, Zhiyun

doi:10.1109/tcsii.2021.3067708

Cited by 20 publications

(13 citation statements)

References 23 publications

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“…Remark 9. Note that some latest studies [34,35] have tried to solve the fault diagnosis problem of discrete systems. However, comparatively speaking, the results of this paper have more technical advantages.…”

Section: State Transition Matrix Methodsmentioning

confidence: 99%

Fault estimation and fault-tolerant control for linear discrete time-varying stochastic systems

Zhang

Deng

Sun

et al. 2021

Sci. China Inf. Sci.

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This paper presents a scheme for simultaneous fault estimation and fault-tolerant control of linear discrete time-varying stochastic systems. An observer is proposed to estimate the system state and the fault simultaneously. The estimation errors of both the system state and fault can achieve exponential stability in mean square sense even if the fault arbitrarily changes or is unbounded. The controllers of the drift term and diffusion term are designed separately, and then based on the estimated fault, the fault compensation is performed to realize fault tolerance. For the parameter design in the estimator and controllers, we provide two different algorithms via the cone complementarity linearization and the state transition matrix methods, respectively. As an extension, a class of quasi-linear systems is also discussed. A simulation example with two different fault types and an application in electromechanical servo systems are provided to illustrate the usefulness of the proposed scheme.

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Section: State Transition Matrix Methodsmentioning

confidence: 99%

Fault estimation and fault-tolerant control for linear discrete time-varying stochastic systems

Zhang

Deng

Sun

et al. 2021

Sci. China Inf. Sci.

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“…Thus, it shows the superiority than some results which either failed to attenuate disturbances [33] or only considered process disturbances [31,32,34,37,38]. Furthermore, completely decoupled conditions utilized in [23,28,35] are not required in our method. By dividing the process disturbances into the decoupled part and the nondecoupled part, our scheme can decouple partial process disturbances, and attenuate the non-decoupled process disturbances via the robust H ∞ optimization technique, leading to a more general design framework.…”

Section: Introductionmentioning

confidence: 91%

“…In light of such models, a close relationship between the fuzzy logic theory and the sophisticated linear systems theory can be established to realize the control design for nonlinear systems. As a result, a series of topics on T-S fuzzy systems have been well discussed [21][22][23][24][25][26][27][28][29][30]. Although many interesting observer design methods have been explored for T-S fuzzy systems, they are not directly applicable for T-S fuzzy singular systems (TSFSSs).…”

Section: Introductionmentioning

confidence: 99%

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Robust observer design for T-S fuzzy singular systems with unmeasurable premise variables and partially decoupled disturbances

Cai

et al. 2022

Preprint

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This article makes some efforts towards discussing the problem of observer design for a kind of discrete-time T-S fuzzy singular systems corrupted by process disturbances and measurement noises. To situate our design scheme in a more practical context, the premise variables of fuzzy systems under consideration are unmeasurable, and the process disturbances are only partially decoupled rather than completely decoupled. By integrating the fuzzy Lyapunov function method and the linear matrix inequality technique, an unknown input observer which can not only decouple partial process disturbances, but also attenuate the influence of measurement noises and non-decoupled process disturbances is proposed to achieve state reconstruction. Particularly, to reduce the conservatism, some slack matrices and scalars are utilized in observer design to obtain extra degrees of freedom. Finally, the performance of theoretical results is fully verified via two numerical simulations.

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“…Estimation accuracy and robustness are introduced as the most important limitations of linear observers [ 18 ]. However, in complex and nonlinear systems, nonlinear estimation techniques, such as feedback linearization [ 17 ], sliding mode [ 10 ], backstepping [ 19 ], Lyapunov-based [ 20 ], fuzzy [ 21 ], and neural network [ 22 ] observers, are suggested. High accuracy and robustness can be the most important positive attributes of the nonlinear compensators [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

Crack Size Identification for Bearings Using an Adaptive Digital Twin

Piltan

Kim

2021

Sensors

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In this research, the aim is to investigate an adaptive digital twin algorithm for fault diagnosis and crack size identification in bearings. The main contribution of this research is to design an adaptive digital twin (ADT). The design of the ADT technique is based on two principles: normal signal modeling and estimation of signals. A combination of mathematical and data-driven techniques will be used to model the normal vibration signal. Therefore, in the first step, the normal vibration signal is modeled to increase the reliability of the modeling algorithm in the ADT. Then, to help challenge the complexity and uncertainty, the data-driven method will solve the problems of the mathematically based algorithm. Thus, first, Gaussian process regression is selected, and then, in two steps, we improve its resistance and accuracy by a Laguerre filter and fuzzy logic algorithm. After modeling the vibration signal, the second step is to design the data estimation for ADT. These signals are estimated by an adaptive observer. Therefore, a proportional-integral observer is then combined with the proposed technique for signal modeling. Then, in two stages, its robustness and reliability are strengthened using the Lyapunov-based algorithm and adaptive technique, respectively. After designing the ADT, the residual signals that are the difference between original and estimated signals are obtained. After that, the residual signals are resampled, and the root means square (RMS) signals are extracted from the residual signals. A support vector machine (SVM) is recommended for fault classification and crack size identification. The strength of the proposed technique is tested using the Case Western Reserve University Bearing Dataset (CWRUBD) under diverse torque loads, various motor speeds, and different crack sizes. In terms of fault diagnosis, the average detection accuracy in the proposed scheme is 95.75%. In terms of crack size identification for the roller, inner, and outer faults, the proposed scheme has average detection accuracies of 97.33%, 98.33%, and 98.33%, respectively.

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State and Fault Estimations for Discrete-Time T-S Fuzzy Systems With Sensor and Actuator Faults

Cited by 20 publications

References 23 publications

Fault estimation and fault-tolerant control for linear discrete time-varying stochastic systems

Fault estimation and fault-tolerant control for linear discrete time-varying stochastic systems

Robust observer design for T-S fuzzy singular systems with unmeasurable premise variables and partially decoupled disturbances

Crack Size Identification for Bearings Using an Adaptive Digital Twin

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