2008
DOI: 10.1016/j.amc.2007.08.059
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Novel delay-dependent stability conditions for a class of MIMO networked control systems with nonlinear perturbation

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Cited by 22 publications
(27 citation statements)
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“…It is well known that nonlinearities commonly exist in practical control systems, so research about networked nonlinear systems is very important in both theory and applications, and it is also a very challenging problem. Lately, there have appeared relevant research results about controller design for networked nonlinear systems (see, e.g., [31][32][33][34][35][36][37][38][39], and references therein). In [31], the authors considered the problem of delay-dependent stability criteria for a class of multi-input and multi-output (MIMO) networked control systems with nonlinear perturbation.…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…It is well known that nonlinearities commonly exist in practical control systems, so research about networked nonlinear systems is very important in both theory and applications, and it is also a very challenging problem. Lately, there have appeared relevant research results about controller design for networked nonlinear systems (see, e.g., [31][32][33][34][35][36][37][38][39], and references therein). In [31], the authors considered the problem of delay-dependent stability criteria for a class of multi-input and multi-output (MIMO) networked control systems with nonlinear perturbation.…”
Section: Introductionmentioning
confidence: 99%
“…Lately, there have appeared relevant research results about controller design for networked nonlinear systems (see, e.g., [31][32][33][34][35][36][37][38][39], and references therein). In [31], the authors considered the problem of delay-dependent stability criteria for a class of multi-input and multi-output (MIMO) networked control systems with nonlinear perturbation. In [32,37], both the authors used a Takagi-Sugeno fuzzy model to represent networked nonlinear systems with both networkinduced delay and data packet dropout, and they both gave the sufficient conditions for the existence of a fuzzy controller and also proposed respective algorithms for the controller design.…”
Section: Introductionmentioning
confidence: 99%
“…Example 1: Consider a networked control time-delay system (1), (4) with parameters taking from (Cao et al, 2007): The networked control time-delay system is robustly asymptotically delay-independent stable. Example 2: Consider a time-delay system (1), (4) with parameters:…”
Section: Numerical Examples and Simulationmentioning
confidence: 99%
“…The five terms sliding mode controllers for time-varying delay systems with structured parameter uncertainties have been designed by Fridman, Gouisbaut, Dambrine and Richard (Fridman et al, 2003) via descriptor approach combined by Lyapunov-Krasovskii functional method. In (Cao et al, 2007) some new delay-dependent stability criteria for multivariable uncertain networked control systems with several constant delays based on Lyapunov-Krasovskii functional combined with descriptor approach and LMI techniques are developed by Cao, Zhong and Hu. A robust sliding mode control of single state delayed uncertain systems with parameter perturbations and external disturbances is designed by Jafarov (Jafarov, 2005).…”
Section: Introductionmentioning
confidence: 99%
“…Jiang et al [16] studied the optimal tracking performance of MIMO NCSs with AWGN channel between the controller and the plant and concluded that the optimal tracking performance was closely dependent on nonminimum phase zeros, unstable poles of the plant, and characteristics of the signals and channel. Cao et al [17] presented delay dependant stability criteria for MIMO NCSs with nonlinear perturbation and delay, which gave much less conservative maximum allowable delay bound. Li et al [18] modeled the MIMO NCS with multichannel packet disordering, packet dropout, and bounded time-varying transmission delay, as a jump linear system subject to Markovian chains, and a real-time controller was proposed such that the cost function value is lower than a specified upper bound.…”
Section: Introductionmentioning
confidence: 99%