2014
DOI: 10.1109/tac.2014.2320308
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Reduction Model Approach for Linear Time-Varying Systems With Delays

Abstract: We study stabilization problems for time-varying linear systems with constant input delays. Our reduction method ensures input-to-state stability with respect to additive uncertainties, under arbitrarily long delays. It applies to rapidly time-varying systems, and gives a lower bound on the admissible rapidness parameters. We also cover slowly time-varying systems, including upper bounds on the allowable slowness parameters. We illustrate our work using a pendulum model.

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Cited by 71 publications
(47 citation statements)
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“…Time-varying systems often arise in tracking problems, even if we use a linear approximation (since the coefficient matrices in the linearization will be time-varying) [39]. It is common in the literature to require the largest possible delay value to be sufficiently small [38], or that certain matrices associated with the vector field are confined to certain compact sets [17,Chapt.…”
Section: Introductionmentioning
confidence: 99%
“…Time-varying systems often arise in tracking problems, even if we use a linear approximation (since the coefficient matrices in the linearization will be time-varying) [39]. It is common in the literature to require the largest possible delay value to be sufficiently small [38], or that certain matrices associated with the vector field are confined to certain compact sets [17,Chapt.…”
Section: Introductionmentioning
confidence: 99%
“…As noticed in [1], time-varying delay has received very little attention. Until very recently, heavily research has done on infinite-time systems with time-varying delay [1,2,3,4,5,6,7,8,9,10,11,12]. With the development of the linear matrix inequality (LMI) approach, robust H∞ controller for time-delay systems has been greatly discussed for stochastic systems [13,14,15,16,17,18,19,20].…”
Section: Introductionmentioning
confidence: 99%
“…Input saturation and input delay frequently appear in practical control engineering and cause performance degradation or even instability. For this reason, many studies have been undertaken during the past two decades [1][2][3][4][5][6][7][8]. In particular, when treating feedforward nonlinear systems, one often assigns small saturation levels to attenuate higher-order terms.…”
Section: Introductionmentioning
confidence: 99%