Robust fast adaptive fault estimation for systems with time-varying interval delay

You, Fang; Li, Hui; Wang, Fuli; Guan, Shouping

doi:10.1016/j.jfranklin.2015.09.006

Cited by 33 publications

(34 citation statements)

References 53 publications

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“…10 to Fig. 13) that fault estimating results are less precise than the existing results [25] while more and more precise as the iterations increase. …”

Section: Compared Examplesmentioning

confidence: 82%

“…= 0.6908 in (25) is satisfied. The comparisons for constant and time-varying fault of the iterative learning maximum estimating error and the estimating error in [25] are shown in Figs 10 and 11, respectively. One can find out that the fault estimating results obtained by this method over the first few iterations are less precise than that in [25].…”

Section: Compared Examplesmentioning

confidence: 99%

“…Time-varying time-delay t d = 1 + 0.5 sin(3t) and discrete sampling time T = 0.01 are taken consideration in this example. The earning matrixes and positive matrixes are developed as follows by using the proposed fault estimator based on iterative = 0.6544 in (25) are satisfied. Figs 12 and 13 show the estimating errors of constant and time-varying faults both in the proposed approach and in the paper [25].…”

Section: Compared Examplesmentioning

confidence: 99%

“…The earning matrixes and positive matrixes are developed as follows by using the proposed fault estimator based on iterative = 0.6544 in (25) are satisfied. Figs 12 and 13 show the estimating errors of constant and time-varying faults both in the proposed approach and in the paper [25]. It can be observed that the results in this paper are more precise than that in [25] after few iterations.…”

Section: Compared Examplesmentioning

confidence: 99%

See 3 more Smart Citations

Observer‐based Fault Estimators Using Iterative Learning Scheme for Linear Time‐delay Systems with Intermittent Faults

Zhang

Chai

et al. 2017

Asian Journal of Control

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This paper deals with the fault estimation problem for a class of linear time-delay systems with intermittent fault and measurement noise. Different from existing observer-based fault estimation schemes, in the proposed design, an iterative learning observer is constructed by using the integrated errors composed of state predictive error and tracking error in the previous iteration. First of all, Lyapunov function including the information of time delay is proposed to guarantee the convergence of system output. Subsequently, a novel fault estimation law based on iterative learning scheme is presented to estimate the size and shape of various fault signals. Upon system output convergence analysis, we proposed an optimal function to select appropriate learning gain matrixes such that tracking error converges to zero, simultaneously to ensure the robustness of the proposed iterative learning observer which is influenced by measurement noise. Note that, an improved sufficient condition for the existence of such an estimator is established in terms of the linear matrix inequality (LMI) by the Schur complements and Young relation. In addition, the results are both suit for the systems with time-varying delay and the systems with constant delay. Finally, three numerical examples are given to illustrate the effectiveness of the proposed methods and two comparability examples are provided to prove the superiority of the algorithm.

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“…10 to Fig. 13) that fault estimating results are less precise than the existing results [25] while more and more precise as the iterations increase. …”

Section: Compared Examplesmentioning

confidence: 82%

Section: Compared Examplesmentioning

confidence: 99%

Section: Compared Examplesmentioning

confidence: 99%

Section: Compared Examplesmentioning

confidence: 99%

See 2 more Smart Citations

Observer‐based Fault Estimators Using Iterative Learning Scheme for Linear Time‐delay Systems with Intermittent Faults

Zhang

Chai

et al. 2017

Asian Journal of Control

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“…In real applications, parameter uncertainties 11,12 are caused by linearization and model reduction, which may influence significantly the performance of fault estimation schemes. Among various types of fault estimation methods that have been developed, observer-based methods have been proven as effective techniques for systems with parameters uncertainties, such as sliding-mode observer (SMO), 6,13 adaptive observer (AO), 15,16 and intermediate observer. 13,14 To cope with these problems, a number of research results available in the literature are presented to estimate the fault signals.…”

Section: Introductionmentioning

confidence: 99%

Iterative learning scheme to design intermittent fault estimators for nonlinear systems with parameter uncertainties and measurement noise

Feng

Chai

et al. 2018

Adaptive Control & Signal

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In this paper, an iterative learning estimator is proposed to deal with period intermittent fault estimation problem in a class of nonlinear uncertain systems. First, state observer is designed for state reconstruction, followed by the Lyapunov function is presented to guarantee the convergence of the system output. Then, the iterative learning scheme-based fault estimator is presented to track the fault signal and the optimal function is established to ensure tracking error convergence. Moreover, linear matrix inequalities and Schur complements are utilized to obtain the sufficient conditions for the existence of iterative learning estimator. Compared with the existing results, error augmented systems should not satisfy the strictly positive realness assumption. Besides, previous state estimation error is used for current fault estimation such that to improve the estimating accuracy. Finally, 2 numerical examples are given to illustrate the effectiveness and validity of the proposed methods. KEYWORDS fault estimation, intermittent fault, iterative learning scheme, LMI, nonlinear uncertain systems 994

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Robust fault estimation based on learning observer for Takagi‐Sugeno fuzzy systems with interval time‐varying delay

YouFuqiang

Cheng

TianKuo

et al. 2019

Adaptive Control & Signal

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SummaryThis paper studies the problem of robust fault estimation for a class of Takagi‐Sugeno(T‐S) fuzzy systems which subject to interval time‐varying delay, external disturbance, and actuator fault. The designed learning observer can achieve simultaneous estimation of system state and time‐varying or constant actuator fault. Then, we construct a new Lyapunov‐Krasovskii functional including the information of the lower and upper delay bounds; compared with the time‐varying delay, the interval time‐varying delay is the less conservative form. Furthermore, one less conservative delay‐dependent condition for the existence of learning observer is given in terms of linear matrix inequalities. In addition, the results for the systems with interval time‐varying delay are simplified when the delay is not concluded. Finally, simulation results of two examples are presented to show the effectiveness of the proposed method.

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Robust fast adaptive fault estimation for systems with time-varying interval delay

Cited by 33 publications

References 53 publications

Observer‐based Fault Estimators Using Iterative Learning Scheme for Linear Time‐delay Systems with Intermittent Faults

Observer‐based Fault Estimators Using Iterative Learning Scheme for Linear Time‐delay Systems with Intermittent Faults

Iterative learning scheme to design intermittent fault estimators for nonlinear systems with parameter uncertainties and measurement noise

Robust fault estimation based on learning observer for Takagi‐Sugeno fuzzy systems with interval time‐varying delay

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