Delay-dependent output-feedback stabilisation of discrete-time systems with time-varying state delay

Gao, Huijun; Lam, James; Wang, C.; Wang, Yaming

doi:10.1049/ip-cta:20040822

Cited by 375 publications

(249 citation statements)

References 13 publications

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“…It is known from [6][7][8] that such conditions might be conservative in some cases. One interesting topic for future research would be how to incorporate the idea embedded in [6][7][8] to obtain an improved delay-dependent result for the problem considered in this paper.…”

Section: Resultsmentioning

confidence: 99%

Discrete-time recurrent neural networks with time-varying delays: Exponential stability analysis

et al. 2007

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This paper is concerned with the analysis problem of exponential stability for a class of discrete-time recurrent neural networks (DRNNs) with time delays. The delay is of the time-varying nature, and the activation functions are assumed to be neither differentiable nor strict monotonic. Furthermore, the description of the activation functions is more general than the recently commonly used Lipschitz conditions. Under such mild conditions, we first prove the existence of the equilibrium point. Then, by employing a Lyapnuov-Krasovskii functional, a unified linear matrix inequality (LMI) approach is developed to establish sufficient conditions for the DRNNs to be globally exponentially stable. It is shown that the delayed DRNNs are globally exponentially stable if a certain LMI is solvable, where the feasibility of such an LMI can be easily checked by using the numerically efficient Matlab LMI Toolbox. A simulation example is presented to show the usefulness of the derived LMI-based stability condition.

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Section: Resultsmentioning

confidence: 99%

Discrete-time recurrent neural networks with time-varying delays: Exponential stability analysis

et al. 2007

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“…On the other hand, most industrial plants have severe nonlinearities, which lead to additional difficulties for the analysis and design of control systems. Though some issues on nonlinear NCSs have been investigated [23,24], limited work has been found on robust   state feedback controller design of networks for fuzzy systems with consideration of both network conditions and signal quantization.…”

Section: Introductionmentioning

confidence: 99%

“…Parallel investigations to the class of switched discrete-time systems with interval time-delays were developed in [19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

H<sub>∞</sub> Control of Uncertain Fuzzy Networked Control Systems with State Quantization

Mahmoud

2012

ICA

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The problem of robust control for uncertain discrete-time Takagi and Sugeno (T-S) fuzzy networked control systems (NCSs) is investigated in this paper subject to state quantization. By taking into consideration network induced delays and packet dropouts, an improved model of network-based control is developed. A less conservative delay-dependent stability condition for the closed NCSs is derived by employing a fuzzy Lyapunov-Krasovskii functional. Robust fuzzy controller is constructed that guarantee asymptotic stabilization of the NCSs and expressed in LMIbased conditions. A numerical example illustrates the effectiveness of the developed technique.

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“…Therefore, such conclusion should be restricted to where HFS and VBR models are used. In this paper, we propose a more practical solution based on LMI (linear matrix inequality) theory [3][4] to the controller design problem for time variant delay systems. The mathematical model of the problem description and assumptions are adopted from [1] [2] with the following modifications: (i) the time invariant delays in [1][2] are now time-variant; (ii) the zeromean i.i.d noise introduced in the AR model for the description of stochastic available bandwidth is now replaced with an energy bounded noise.…”

Section: Introductionmentioning

confidence: 99%

Robust Congestion Control for High Speed Data Networks with Uncertain Time-Variant Delays: an LMI Control Approach

Lin

Xie

2005

The IEEE Conference on Local Computer Networks 30th Anniversary (LCN'05)l

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Delay-dependent output-feedback stabilisation of discrete-time systems with time-varying state delay

Cited by 375 publications

References 13 publications

Discrete-time recurrent neural networks with time-varying delays: Exponential stability analysis

Discrete-time recurrent neural networks with time-varying delays: Exponential stability analysis

H<sub>∞</sub> Control of Uncertain Fuzzy Networked Control Systems with State Quantization

Robust Congestion Control for High Speed Data Networks with Uncertain Time-Variant Delays: an LMI Control Approach

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Delay-dependent output-feedback stabilisation of discrete-time systems with time-varying state delay

Cited by 375 publications

References 13 publications

Discrete-time recurrent neural networks with time-varying delays: Exponential stability analysis

Discrete-time recurrent neural networks with time-varying delays: Exponential stability analysis

H&lt;sub&gt;∞&lt;/sub&gt; Control of Uncertain Fuzzy Networked Control Systems with State Quantization

Robust Congestion Control for High Speed Data Networks with Uncertain Time-Variant Delays: an LMI Control Approach

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H<sub>∞</sub> Control of Uncertain Fuzzy Networked Control Systems with State Quantization