An output delay approach to fault estimation for sampled-data systems

Wen, Chenglin; Qiu, Aibing; Jiang, Bin

doi:10.1007/s11432-011-4472-8

Cited by 11 publications

(10 citation statements)

References 24 publications

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“…It is a common practice to use the delay system approach in dealing with sampling effects in the system [22,[29][30][31].…”

Section: And a Weighted Adjacency Matrixmentioning

confidence: 99%

“…(i) by setting the auxiliary function i = 0, calculate the matrices D i according to the asymptotic stability of the estimation error system (10) under fault free condition; (ii) solve the LMI (22) to obtain the unknown matrices E i and F i from (24); (iii) the distributed state estimation error system (10) can be established.…”

Section: Theorem 1 Given a Positive Scalar And Matricesmentioning

confidence: 99%

“…As is well known, faults occur in network control designs due to the uncertain environment or disturbances. Over past decades, fault analysis has been a significant research topic [18][19][20][21][22][23][24][25]. For example, fault detection for linear discrete-time, periodic, and sampled-data system is investigated in [26].…”

Section: Introductionmentioning

confidence: 99%

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Sampled‐Data Approach to State Estimation Performance for Heterogeneous Distributed System with Fault

Chen¹,

Ho²

2014

Asian Journal of Control

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This paper considers the state estimation performance for heterogeneous distributed system with fault based on sampled-data measurement. Firstly, a performance index for distributed state estimation is proposed. Based on the sampled-data measurement of the heterogeneous distributed system, distributed state estimators are constructed. It is proved that by analyzing the distributed state estimation error systems, the required performance index for overall state estimation can be obtained using linear matrix inequality method. Finally, a simulation example is used to illustrate the effectiveness of the proposed theoretical scheme.

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“…It is a common practice to use the delay system approach in dealing with sampling effects in the system [22,[29][30][31].…”

Section: And a Weighted Adjacency Matrixmentioning

confidence: 99%

Section: Theorem 1 Given a Positive Scalar And Matricesmentioning

confidence: 99%

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Sampled‐Data Approach to State Estimation Performance for Heterogeneous Distributed System with Fault

Chen¹,

Ho²

2014

Asian Journal of Control

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“…In [21], we have touched on fault estimation for non uniformly sampled-data systems, which constitutes a basis of Theorem 1. The main differences between the two are as follows.…”

Section: Remarkmentioning

confidence: 99%

“…Based on our preliminary work on fault estimation for sampled-data systems [21], a framework of fault estimation and accommodation is proposed for nonuniformly sampled NCSs in this paper. Comparing with the existing work on active FTC for NCSs, the novelties of this paper are summarized in three aspects.…”

Section: Introductionmentioning

confidence: 99%

Fault estimation and accommodation for networked control systems with nonuniform sampling periods

Qiu

Jiang

Wen

et al. 2014

Adaptive Control & Signal

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This paper deals with the problem of fault estimation and accommodation for a class of networked control systems with nonuniform uncertain sampling periods. Firstly, the reason why the adaptive fault diagnosis observer cannot be applied to networked control systems is analyzed. Based on this analysis, a novel robust fault estimation observer is constructed to estimate both continuous-time fault and system states by using nonuniformly discrete-time sampled outputs. Furthermore, using the obtained states and fault information, a nonuniformly sampled-data fault tolerant control law is designed to preserve the stability of the closed-loop system. The proposed scheme can not only guarantee the impact of continuous-time uncertainties and discrete-time sampled estimation errors on the faulty system to satisfy a H 1 performance index but also repress the negative effect of the unknown intersample behavior of continuous-time fault by use of an inequality technique. Finally, simulation results are included to demonstrate the feasibility of the proposed method. Although no time delays and packet dropouts during the data transfer are assumed in the former NCS, it can also express a general framework of NCSs with packet dropouts. Considered the packet dropouts, the input and output signal are updated when the new information is arrived. If the sampling and updating intervals contain the packet dropouts' times, the nonuniformly sampled NCS (1), (3)-(4) can describe the NCSs with dropouts. This statement has been made in [14] and [25]. Remark 2According to the former proof, the core idea of the adaptive fault diagnosis observer is to take e x .t / and e f .t / apart to construct the Lyapunov function. Correspondingly, in the derivative of the Lyapunov function, there exists two cross items of e x .t /; e f .t /, but no quadratic item of e f .t /. Thus one has to choose Lyapunov matrices P , 1 and make equality (10) holds to remove the two cross items.

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$$\mathcal {H}_{\infty }$$ H ∞ filtering for sample data systems with stochastic sampling and Markovian jumping parameters

et al. 2014

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An output delay approach to fault estimation for sampled-data systems

Cited by 11 publications

References 24 publications

Sampled‐Data Approach to State Estimation Performance for Heterogeneous Distributed System with Fault

Sampled‐Data Approach to State Estimation Performance for Heterogeneous Distributed System with Fault

Fault estimation and accommodation for networked control systems with nonuniform sampling periods

$$\mathcal {H}_{\infty }$$ H ∞ filtering for sample data systems with stochastic sampling and Markovian jumping parameters

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