Unknown Input Method Based Observer Synthesis for a Discrete Time Uncertain T–S Fuzzy System

Vu, Van‐Phong; Wang, Wen-June; Żurada, Jacek M.; Chen, Hsiang-Chieh; Chiu, Chih-Hui

doi:10.1109/tfuzz.2017.2688388

Cited by 50 publications

(31 citation statements)

References 48 publications

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“…T ; m i (x(t )) and m i (x(t )) are obtained by rewriting m i ( (t )) in (2) and m i ( (x(t ))) in (27), respectively. The expressions m i (x(t )) and m i (x(t )) are helpful for clearly demonstrating the relationship between the NMFs of the model and those of the controller.…”

Section: Ftc Schemementioning

confidence: 99%

“…With Definition 1, the FTC problem is stated as follows: P2. For a given scalar > 0, design an FTC law (27) so that the closed-loop system (28) has an L ∞ performance bound . □ Next, we will establish a criterion on the existence of the fault tolerant controller (27) such that Problem P2 can be addressed.…”

Section: Definitionmentioning

confidence: 99%

“…For a given scalar > 0, design an FTC law (27) so that the closed-loop system (28) has an L ∞ performance bound . □ Next, we will establish a criterion on the existence of the fault tolerant controller (27) such that Problem P2 can be addressed. 3 and 4 , under the control law (27), the system (28) has an L ∞ performance bound if there exist matricesW > 0, R > 0,…”

Section: Definitionmentioning

confidence: 99%

“…Therefore, in order to decrease the difficulty for solving (53), the deviation bound i should be reduced. In this paper, combining with the L ∞ technology, a new fault compensation mechanism is established in the premise part of the fuzzy controller (27) to reduce i such that design conservatism can be further decreased.…”

Section: The Termmentioning

confidence: 99%

“…To further demonstrate the advantage of the proposed fault compensation mechanism, the developed method, where the fault compensation mechanism is established in the premise part of the controller, is compared with the FTC scheme (27), where there is no fault compensation mechanism in the premise part of the controller. (The stabilization conditions Figure 2.…”

Section: Examplementioning

confidence: 99%

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A new fault tolerant control scheme for non‐linear systems by T‐S fuzzy model approach

Yang

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IET Control Theory & Appl

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This paper is concerned with the problem of fault tolerant control for non-linear systems with sensor faults based on the T-S fuzzy model approach. First, a fault estimation (FE) observer with L ∞ technique is designed to estimate the fault. Second, by using the fault estimation information, which are provided by the fault estimation observer, a novel fault tolerant control scheme with a fault compensation mechanism in the premise part of the fuzzy controller is presented to compensate the effect of the fault and stabilize the system. Different from the existing methods, where the fault compensation mechanism is only established in the consequent part of the controller, in the presented method, the fault compensation mechanism is established in the consequent part as well as the premise part of the fuzzy controller. Then, by using the fault compensation mechanism, the bound of the deviation between the normalized membership functions of the fuzzy model and those of the controller can be reduced such that the fault tolerant control design can be relaxed. In the end, an example is given to illustrate the validity of the developed fault tolerant control scheme.

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Section: Ftc Schemementioning

confidence: 99%

Section: Definitionmentioning

confidence: 99%

Section: Definitionmentioning

confidence: 99%

Section: The Termmentioning

confidence: 99%

Section: Examplementioning

confidence: 99%

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Anticipating the Loss of Unknown Input Observability for Sampled LPV Systems

Bainier

Ponsart

Marx

2023

Studies in Systems, Decision and Control

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Given a continuous-time Linear Parameter-Varying (LPV) system with a sampled scheduling parameter and subject to an unknown input, this paper provides -under some Lipschitz assumptions -an exact discretization of an extended system which translates the sampled-data unknown input estimation problem into a discretetime LPV observer design problem with norm-bounded uncertainties. The bounds developed in this process account for the inter-sample behavior of the scheduling parameter, and allow for an estimation of some near-future observability Gramians, from which it is possible to lower bound the number of samples for which the unknown input is guaranteed to remain observable.

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Fault Estimation and Tolerant Control for Multiple Time Delayed Switched Fuzzy Stochastic Systems with Sensor Faults and Intermittent Actuator Faults

Sun

et al. 2023

Intelligent Control and Learning Systems

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Unknown Input Method Based Observer Synthesis for a Discrete Time Uncertain T–S Fuzzy System

Cited by 50 publications

References 48 publications

A new fault tolerant control scheme for non‐linear systems by T‐S fuzzy model approach

A new fault tolerant control scheme for non‐linear systems by T‐S fuzzy model approach

Anticipating the Loss of Unknown Input Observability for Sampled LPV Systems

Fault Estimation and Tolerant Control for Multiple Time Delayed Switched Fuzzy Stochastic Systems with Sensor Faults and Intermittent Actuator Faults

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