Improved inequality-based functions approach for stability analysis of time delay system

Li, Zhichen; Yan, Huaicheng; Zhang, Hao; Zhan, Xisheng; Huang, Congzhi

doi:10.1016/j.automatica.2019.05.033

Cited by 90 publications

(35 citation statements)

References 33 publications

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“…For different u, Table 1 presents the allowable upper bound of h(t), which guarantees the stability of system (1). Table 1 shows that our method produces the larger upper bound h than those in [7,12,13,16,17,21]. In this sense, our stability criterion is less conservative than those in [7,12,13,16,17,21].…”

Section: Numerical Examplesmentioning

confidence: 96%

“…e integral inequality method includes various integral inequalities, such as Jensen inequality [9][10][11], Wirtinger-based inequality [12][13][14][15], free matrix-based inequality [16,17], auxiliary function-based inequality [18], relaxed integral inequality [19], and Bessel-Legendre inequality [20]. Very recently, the improved inequality-based functions approach [21] is proposed to derive less conservative results for systems with time-varying delays. However, when…”

Section: Introductionmentioning

confidence: 99%

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A New Stability Criterion for Systems with Distributed Time-Varying Delays via Mixed Inequalities Method

Tian

Liu

2020

Complexity

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This paper is concerned with the delay-dependent stability of systems with distributed time-varying delays. The novelty relies on the use of some new inequalities which are less conservative than some existing inequalities. A less conservative stability criterion is obtained by constructing some new augmented Lyapunov–Krasovskii functionals, which are given in terms of linear matrix inequalities. The effectiveness of the presented criterion is demonstrated by two numerical examples.

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Section: Numerical Examplesmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

A New Stability Criterion for Systems with Distributed Time-Varying Delays via Mixed Inequalities Method

Tian

Liu

2020

Complexity

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“…where l represents weight decay coefficient that controls the relative importance of the two terms in equation (5). W (l) ji represents the synaptic weight between the ith neuron in layer l and jth neuron in layer l + 1. n l represents the number of layers in AE.…”

Section: The Training Of Aementioning

confidence: 99%

Fault diagnosis of motor bearing based on deep learning

Jian

Qing

et al. 2019

Advances in Mechanical Engineering

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The effective fault diagnosis of the motor bearings not only can ensure the smooth and efficient operation of equipment but also can detect and eliminate the running fault in time to prevent major accidents. Based on deep learning algorithm, this article constructs a stacked auto-encoder network. The input data are compressed and reduced by introducing sparsity constraint, so that the network can accurately extract the fault characteristics of the input data, and the fault recognition ability of the network can be improved by introducing random noise. The simulation result shows that the stacked auto-encoder network can not only overcome the shortcomings of traditional fault diagnosis method that requires to distinguish fault samples manually and needs a large number of prior knowledge but also realize the self-learning of fault signal feature. The accuracy rate of fault identification reaches 98%, 94%, 96%, and 95.5% in four different working conditions. What’s more, the network can exhibit strong robustness under different working conditions. Finally, the new research ideas of fault diagnosis in thermal power plant are put forward by copying the idea of fault diagnosis of motor bearing.

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“…In [17], the stability analysis of improved delay-dependent NCSs have been conducted. The stabilization of NCSs under clock mismatches and quantization have been investigated in [18].…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of NCSs Under Channel Noise and Bandwidth Constraints

Zhan

Zhang

et al. 2020

IEEE Access

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This paper investigates the performance limitation of multi-input multi-output (MIMO) networked control systems (NCSs). The communication channel is modeled as a power constrained channel with channel noise and the bandwidth constraints. The optimal performance index of the system is expressed by the covariance of the reference input signal and the system output error signal. Some new results about MIMO NCSs are obtained by co-prime factorization, inner-outer factorization and spectral decomposition techniques. The results demonstrate that the tracking performance depends on the non-minimum phase zeros, unstable poles and their directions. At the same time, the network communication parameters constraints, energy constraints and the essential feature of reference input signal also restrict the performance. Some illustrative examples are presented to demonstrate the feasibility of the proposed methods. INDEX TERMS Bandwidth constrains, channel noise, communication channels, performance limitation.

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Improved inequality-based functions approach for stability analysis of time delay system

Cited by 90 publications

References 33 publications

A New Stability Criterion for Systems with Distributed Time-Varying Delays via Mixed Inequalities Method

A New Stability Criterion for Systems with Distributed Time-Varying Delays via Mixed Inequalities Method

Fault diagnosis of motor bearing based on deep learning

Performance Analysis of NCSs Under Channel Noise and Bandwidth Constraints

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