Robust reliable H∞ control for neural networks with mixed time delays

Du, Yuanhua; Liu, Xinzhi; Zhong, Shouming

doi:10.1016/j.chaos.2016.04.009

Cited by 20 publications

(10 citation statements)

References 23 publications

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“…Furthermore, if we choose g(u(t)) = 0, the controller it changes to the traditional feedback reliable controller. Therefore, the considered reliable controller is more general compare with traditional controllers in existing works [28,40].…”

Section: Problem Formulationmentioning

confidence: 99%

“…A huge degree of fault tolerance pertaining to the operational systems is an important and integrated part of the closed-loop system. Thus, the result of fault-tolerant control in dynamical systems was considered and several types of fault-tolerant control techniques have been investigated [28,29]. Saravanakumar et al [30] investigated the problem of dissipative control for discrete-time dynamic control systems through reliable control with respect to actuator faults.…”

Section: Introductionmentioning

confidence: 99%

“…Substituting (28) into (27) yields E { x T (t)Lx(t) } < c 2 . Therefore from the Definition 2.2, the neutral-type of switched ANN in (6) with u f (t) = 0 and without randomly occurring uncertainties is finite time bounded.…”

mentioning

confidence: 99%

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Finite‐time reliable dissipative control of neutral‐type switched artificial neural networks with non‐linear fault inputs and randomly occurring uncertainties

Saravanakumar

Nirmala²,

Raja

et al. 2019

Asian Journal of Control

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This work reports the result of finite-time reliable dissipative control for switched neutral-type based neural networks subject to input nonlinearities, time-delay and randomly occurring perturbations. In particular, a novel control model consist of both linear and non-linear fault inputs is designed for reliable control. In addition, randomly occurring uncertainty caused by stochastic variables satisfying the Bernoulli distribution is considered. Based on Lyapunov stability theory, Jensen's integral inequality technique and average dwell time method, sufficient criteria for finite-time boundedness of resulting neural networks are obtained. Furthermore, the obtained boundedness theory are enhanced to solve the finite-time dissipative problem of the considered systems through reliable control against mixed actuator failures. The obtained sufficient conditions are constructed in the form of linear matrix inequalities, which can be facilitated by using some standard numerical Matlab packages. At last, numerical simulations are presented to demonstrate the effectiveness of the considered theory. KEYWORDS finite-time boundedness, finite-time dissipative, neutral-type of switched neural networks, reliable control, time-delay How to cite this article: Saravanakumar T, Nirmala VJ, Raja R, Cao J, Lu G. Finite-time reliable dissipative control of neutral-type switched artificial neural networks with nonlinear fault inputs and randomly occurring uncertainties.

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Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Finite‐time reliable dissipative control of neutral‐type switched artificial neural networks with non‐linear fault inputs and randomly occurring uncertainties

Saravanakumar

Nirmala²,

Raja

et al. 2019

Asian Journal of Control

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“…On the other hand, the performance of neural networks has been analyzed by a variety of techniques, which often have input and output relationships, and they play an important role in science and engineering applications. For example, Du et al [19] studied the problem of robust reliable H ∞ control for neural networks with mixed time delays based on the LMI technique and the Lyapunov stability theory. In [20], the problem of finite-time nonfragile passivity control for neural networks with time-varying delay is investigated based on a new Lyapunov-Krasovskii function with tripple and quadruple integral terms and utilizing Wirtinger-type inequality technique.…”

Section: Introductionmentioning

confidence: 99%

Novel extended dissipativity criteria for generalized neural networks with interval discrete and distributed time-varying delays

Luemsai

Botmart

Weera³

2021

Adv Differ Equ

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The problem of asymptotic stability and extended dissipativity analysis for the generalized neural networks with interval discrete and distributed time-varying delays is investigated. Based on a suitable Lyapunov–Krasovskii functional (LKF), an improved Wirtinger single integral inequality, a novel triple integral inequality, and convex combination technique, the new asymptotic stability and extended dissipativity criteria are achieved for the generalized neural networks with interval discrete and distributed time-varying delays. By the above methods, the less conservative asymptotic stability criteria are obtained for a special case of the generalized neural networks. By using the Matlab LMI toolbox, the derived new asymptotic stability and extended dissipativity criteria are expressed in terms of linear matrix inequalities (LMIs) that cover $H_{\infty }$ H ∞ , $L_{2}$ L 2 –$L_{\infty }$ L ∞ , passivity, and dissipativity performance by setting parameters in the general performance index. Finally, we show numerical examples which are less conservative than other examples in the literature. Moreover, we present numerical examples for asymptotic stability and extended dissipativity performance of the generalized neural networks, including a special case of the generalized neural networks.

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“…Most of works have been focused on the problem of designing a robust H ∞ controller that stabilizes linear uncertain systems with time-varying norm bounded parameter uncertainty in the state and input matrices. The problem of designing a robust reliable H ∞ controller for neural networks is considered in [23]. Ref [24] have studied the problem of delay dependent robust H ∞ control for a class of uncertain systems with distributed time-varying delays.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Feedback Control for Exponential Stability and Robust H∞ Control of a Class of Uncertain Neural Network with Mixed Interval and Distributed Time-Varying Delays

et al. 2021

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This paper is concerned the problem of robust H∞ control for uncertain neural networks with mixed time-varying delays comprising different interval and distributed time-varying delays via hybrid feedback control. The interval and distributed time-varying delays are not necessary to be differentiable. The main purpose of this research is to estimate robust exponential stability of uncertain neural network with H∞ performance attenuation level γ. The key features of the approach include the introduction of a new Lyapunov–Krasovskii functional (LKF) with triple integral terms, the employment of a tighter bounding technique, some slack matrices and newly introduced convex combination condition in the calculation, improved delay-dependent sufficient conditions for the robust H∞ control with exponential stability of the system are obtained in terms of linear matrix inequalities (LMIs). The results of this paper complement the previously known ones. Finally, a numerical example is presented to show the effectiveness of the proposed methods.

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Robust reliable H∞ control for neural networks with mixed time delays

Cited by 20 publications

References 23 publications

Finite‐time reliable dissipative control of neutral‐type switched artificial neural networks with non‐linear fault inputs and randomly occurring uncertainties

Finite‐time reliable dissipative control of neutral‐type switched artificial neural networks with non‐linear fault inputs and randomly occurring uncertainties

Novel extended dissipativity criteria for generalized neural networks with interval discrete and distributed time-varying delays

Hybrid Feedback Control for Exponential Stability and Robust H∞ Control of a Class of Uncertain Neural Network with Mixed Interval and Distributed Time-Varying Delays

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