Robust finite-time H∞ control for a class of uncertain switched neural networks of neutral-type with distributed time varying delays

Ali, M. Syed; Saravanan, S.

doi:10.1016/j.neucom.2015.11.058

Cited by 52 publications

(17 citation statements)

References 45 publications

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“…Remark It is noteworthy that the constructed LKFs V k ( z ( t ),σ t ), k =1,2,3,4 are more general than the ones in References and play a key role in deriving the stochastic robust finite‐time bounded result. Especially the vectors

η_{1} (α) = col {z (α), z (α + δ_{1})}, η_{2} (α) = col {z (α), z (α + δ_{2})}, η_{4} (α) = col {z (α), ż (α)}

in V 2 ( z t ,σ t ), which contain more information about additive TDs and neutral‐type TD.…”

Section: Resultsmentioning

confidence: 99%

“…It is necessary to note that classical Lyapunov stability and finite‐time stability are two independent concepts, a system is Lyapunov stability might not be finite‐time stability, and vice versa. Consequently, the finite‐time boundedness (FB) problem of NNs with mixed TDs has attracted much more attention, ever since the concept of FS was extended to FB by taking into account the presence of external disturbances and parametric uncertainties …”

Section: Introductionmentioning

confidence: 99%

“…It is necessary to note that classical Lyapunov stability and finite-time stability are two independent concepts, a system is Lyapunov stability might not be finite-time stability, and vice versa. Consequently, the finite-time boundedness (FB) problem of NNs with mixed TDs has attracted much more attention, [27][28][29] ever since the concept of FS was extended to FB by taking into account the presence of external disturbances and parametric uncertainties. 30 On the other hand, the structure and parameters of many physical systems in the actual operation are often subject to random abrupt changes, such as component and/or interconnection failures, unexpected environmental shifts, and so on.…”

mentioning

confidence: 99%

“…[36][37][38] To the best of our knowledge, however, there are few pieces of literature that discussed the SRFB problem for SMJU-NNNs with distributed and additive TDs. As mentioned above, previous works [27][28][29] only investigated the FB problems for NNNs with distributed TD but did not consider the additive TDs and SMJU parameters. Furthermore, the SMJU parameters are not considered in References 22-24, and the distributed and additive TDs are not taken into account in References 36-38.…”

mentioning

confidence: 99%

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Stochastic robust finite‐time boundedness for semi‐Markov jump uncertain neutral‐type neural networks with mixed time‐varying delays via a generalized reciprocally convex combination inequality

Zhang

Qiu

Liu

et al. 2019

Intl J Robust & Nonlinear

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Summary This article investigates the stochastic robust finite‐time boundedness problem for semi‐Markov jump uncertain (SMJU) neutral‐type neural networks with distributed and additive time‐varying delays (TDs). To derive less conservative stability criteria, a generalized reciprocally convex combination inequality (RCCI) is first proposed, which includes the existing RCCIs as its special cases. By taking full advantage of the characteristics of various TDs and SMJU parameters, a novel suitable Lyapunov‐Krasovskii functional is provided. Then, with the virtue of the new RCCI and other analysis approaches, some new criteria guaranteeing the underlying systems are stochastically robustly finite‐time bounded or stable and are derived in the form of linear matrix inequalities. Finally, three numerical examples are given to show the validity of the approaches presented in this article.

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η_{1} (α) = col {z (α), z (α + δ_{1})}, η_{2} (α) = col {z (α), z (α + δ_{2})}, η_{4} (α) = col {z (α), ż (α)}

in V 2 ( z t ,σ t ), which contain more information about additive TDs and neutral‐type TD.…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Stochastic robust finite‐time boundedness for semi‐Markov jump uncertain neutral‐type neural networks with mixed time‐varying delays via a generalized reciprocally convex combination inequality

Zhang

Qiu

Liu

et al. 2019

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

show abstract

“…The analysis of uncertainties is another hot topic for studying dynamic systems [24][25][26][27] . However, reducing conservatism in such systems is normally accompanied with extra computational complexity 28 .…”

mentioning

confidence: 99%

Improved Criteria on Robust Analysis for Linear System Using Convex Combination and Geometric Sequence Methods

2017

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This article addresses the robust analysis on a delayed system with uncertainties. A geometric sequence division (GSD) method is applied for delay partition. Then, a GSD-dependent Lyapunov-Krasovskii functional (LKF) is newly proposed, in which the integral interval relevant with the state variables forms in geometric progression. In addition, by applying the convex combination method, parameter uncertainties and the delay derivative ( )  d t can thus be flexibly overcome. As a result, unnecessary enlargement for estimating the LKF derivative is eliminated. Numerical example shows that this proposed work achieves expected results.

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Neural network–based direct adaptive robust control of unknown MIMO nonlinear systems using state observer

Cheng

Liu

et al. 2019

Adaptive Control & Signal

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SummaryThis paper focuses on the problem of adaptive robust tracking control for a class of uncertain multiple‐input and multiple‐output (MIMO) nonlinear system. Unlike most previous research studies, model dynamics, disturbances, and state variables are unknown in this paper. A novel observer‐based direct adaptive neuro‐sliding mode control approach is proposed of which the only required knowledge is the system output. By incorporating the Adaptive Linear Neuron (ADALINE) neural network (NN) into the conventional sliding mode observer, the proposed observer has favorable performance. In the controller, a radial basis function (RBF) NN is constructed to approximate the unknown equivalent control laws and the estimation of the sliding surface is applied as the input. A gain‐adaptation sliding mode term is designed to enhance the robustness of the control system. Besides, the free parameters of the ADALINE NN and the RBFNN are updated online by adaptive laws to obtain optimal approximation performance. Finally, the comparative simulations are given to show the effectiveness and merits of proposed scheme.

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Robust finite-time H∞ control for a class of uncertain switched neural networks of neutral-type with distributed time varying delays

Cited by 52 publications

References 45 publications

Stochastic robust finite‐time boundedness for semi‐Markov jump uncertain neutral‐type neural networks with mixed time‐varying delays via a generalized reciprocally convex combination inequality

Stochastic robust finite‐time boundedness for semi‐Markov jump uncertain neutral‐type neural networks with mixed time‐varying delays via a generalized reciprocally convex combination inequality

Improved Criteria on Robust Analysis for Linear System Using Convex Combination and Geometric Sequence Methods

Neural network–based direct adaptive robust control of unknown MIMO nonlinear systems using state observer

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