Event‐triggered non‐fragile  filtering of linear systems with a structure separated approach

Yan, Shen; Shen, Mouquan; Che, Wei‐Wei; Zhang, Guangming

doi:10.1049/iet-cta.2017.0421

Cited by 12 publications

(8 citation statements)

References 33 publications

(26 reference statements)

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“…] . Further, by Lemma 2.1 of Sathishkumar et al, 25 there exist scalars 𝜖 4 and 𝜖 5 such that the above inequality along with Schur complement lemma implies the LMI in (18). Thus, the stochastic stability of the filtering error system (5) in the mean square is concluded with a prescribed mixed H ∞ and passivity performance index 𝛾 > 0.…”

Section: Quantized Mixed H ∞ and Passivity-based Filter Designmentioning

confidence: 83%

“…Now, the results are extended by considering the time-varying filter implementation uncertainties. Incorporating the structure of gain perturbations defined in (4) for the LMI terms in (18) consisting of fluctuations and employing Lemma 2.1 of Sathishkumar et al 25 and Schur complement lemma, the LMIs ( 17), (18) in Theorem 2 are equivalent to the LMIs ( 22), (23) in Theorem 3. Moreover, from these feasible results, the quantized nonfragile filter gain parameters are calculated as…”

Section: Quantized Mixed H ∞ and Passivity-based Nonfragile Filter De...mentioning

confidence: 99%

“…In practical situations sensor failures, missing measurements and communication delays are inevitable in the networks due to unexpected changes. However, great number of efforts on filtering problems have been devoted with missing measurements in recent years 14–27 . In real‐world circumstances, parameter uncertainties are treated as unavoidable factors because of sudden environmental changes, modeling errors, measurement inaccuracies, and so on.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Quantized filtering for networked Takagi–Sugeno fuzzy systems with multipath data packet dropouts

Nithya¹,

Sakthivel

Kong

et al. 2023

Math Methods in App Sciences

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This paper concerns the mixed H ∞ and passivity-based filtering problem for networked nonlinear systems with randomly occurring parameter uncertainties, multipath data packet dropouts, time-varying delay, and quantization effects.The discrete-time nonlinear plant considered in this paper is modeled as a Takagi-Sugeno fuzzy system with plant rules. The data missing phenomenon is considered in both measurement and performance output signals due to the unreliable nature of communication links. Further, to reduce the network bandwidth utilization, the measurement quantization is also employed for both the outputs before transmitting through the communication channel. Also, additive filter gain fluctuation is taken into account to reflect the imprecision in filter implementation. Stochastic variables obeying the Bernoulli distribution are incorporated in the model description to characterize the random occurrence of parameter uncertainties and data packet dropouts. A set of sufficient conditions assuring the stochastic stability of the filtering error system with mixed H ∞ and passivity performance is derived according to Lyapunov stability theory. Finally, three numerical examples including mass-spring-damper and tunnel diode circuit model are established to establish the validity of the developed filter design algorithm.

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Section: Quantized Mixed H ∞ and Passivity-based Filter Designmentioning

confidence: 83%

Section: Quantized Mixed H ∞ and Passivity-based Nonfragile Filter De...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Quantized filtering for networked Takagi–Sugeno fuzzy systems with multipath data packet dropouts

Nithya¹,

Sakthivel

Kong

et al. 2023

Math Methods in App Sciences

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“…By modeling multiplicative gain variations in the non-fragile estimator, we can ensure that the estimator remains accurate even in the presence of these fluctuations. 29 Incorporating both additive and multiplicative gain variations models in the nonfragile estimator is important because it can provide a more complete representation of the variations that can arise in the system. This can help to ensure that the estimator is robust to a wide range of disturbances and can provide accurate state estimates in a variety of operating conditions.…”

Section: Event-triggering Mechanismmentioning

confidence: 99%

Event‐triggered nonfragile state estimation for delayed neural networks with additive and multiplicative gain variations

Karnan

Nagamani

2023

Intl J Robust & Nonlinear

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This article aims to design a nonfragile state estimator for neural networks with time-varying delay under the event-triggered mechanism. This paper addresses the two most common issues that exist in large-scale networks: gain variations in the estimator design and lack of network resources. Based on the nonfragile paradigm and an event-triggered mechanism, an estimator is being developed to address this issue, considering both the additive and multiplicative structured gain variations. Through the Lyapunov-Krasovskii functional method and linear matrix inequality technique, sufficient conditions are derived to guarantee the existence of the proposed state estimator by employing the generalized free-weighting matrix inequality and improved reciprocally convex inequality.Moreover, the obtained linear matrix inequalities provide an explicit expression of the estimator gain matrix and triggering matrix. Eventually, two numerical examples are provided to authenticate the proposed findings.

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“…During the past decades, numerously fast-growing interest has been focused on the investigation on non-fragile control or filtering problem of practical systems (Liu G et al, 2018; Yan et al, 2017). Due to physical and safety constraints, one of the common control problems is input constraints.…”

Section: Introductionmentioning

confidence: 99%

A novel robust non-fragile control approach for a class of uncertain linear systems with input constraints

Shi

Liu

Sun

et al. 2018

Transactions of the Institute of Measurement and Control

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This paper addresses the non-fragile control problem for a class of uncertain linear systems subject to model uncertainty, controller perturbations, fault signals and input constraints. The controller to be designed is supposed to have additive gain perturbations. A novel state feedback controller is proposed based on the exact available expectation of a Bernoulli random variable, which is introduced to model the feature of the controller gain perturbation that randomly occurs. By using Lyapunov stability theory, new sufficient conditions are derived to design non-fragile controller for a class of uncertain linear systems considering input constraints. Compared with the existing non-fragile state feedback controller methods, the non-fragile property is fully considered to improve the tolerance of uncertainties in the controller, where the conservativeness can be reduced via the Bernoulli random variable. The effectiveness of the proposed control strategy is illustrated by two numerical examples.

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Event‐triggered non‐fragile filtering of linear systems with a structure separated approach

Cited by 12 publications

References 33 publications

Quantized filtering for networked Takagi–Sugeno fuzzy systems with multipath data packet dropouts

Quantized filtering for networked Takagi–Sugeno fuzzy systems with multipath data packet dropouts

Event‐triggered nonfragile state estimation for delayed neural networks with additive and multiplicative gain variations

A novel robust non-fragile control approach for a class of uncertain linear systems with input constraints

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