2003
DOI: 10.1109/tac.2003.816966
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A unified framework for input-to-state stability in systems with two time scales

Abstract: This paper develops a unified framework for studying robustness of the input-to-state stability (ISS) property and presents new results on robustness of ISS to slowly varying parameters, to rapidly varying signals, and to generalized singular perturbations. The common feature in these problems is a timescale separation between slow and fast variables which permits the definition of a boundary layer system like in classical singular perturbation theory. To address various robustness problems simultaneously, the… Show more

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Cited by 189 publications
(94 citation statements)
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References 59 publications
(69 reference statements)
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“…R. Teel and Aeyels, 1999) and singular perturbations (Christofides and Teel, 1996;Teel et al, 2003) that are tailored for analysis of semi-global practical stability of systems that exhibit time scale separation. Similar to we show that the closed loop system with an extremum seeking controller exhibits two time scales.…”
Section: Introductionmentioning
confidence: 99%
“…R. Teel and Aeyels, 1999) and singular perturbations (Christofides and Teel, 1996;Teel et al, 2003) that are tailored for analysis of semi-global practical stability of systems that exhibit time scale separation. Similar to we show that the closed loop system with an extremum seeking controller exhibits two time scales.…”
Section: Introductionmentioning
confidence: 99%
“…Proof of this result is omitted owing to space limitation, it relies upon the results for two-time scale averaged systems given in (Teel et al, 2003) (see in particular Theorem 1). Analyzing the previous result, it can be clarified how the proposed controller satisfies the objectives in a practical way; by the second of (75) we deduce thatθ tends arbitrary close to the power loss term ϕ 0 , while by the first of (75), it can be seen that the power vector x is steered arbitrary close to the reference value x * ϕ 0 .…”
Section: Overall System Stabilitymentioning
confidence: 99%
“…LMA forms the mathematical foundation for both the Safety Assurance Monitor (SAM) [11,12,13,14,15] and Correct-by-Construction Artificial Neural Network (CCANN) [8,9,10] which are two applications of the same technology. For an introduction to LMA see the appendix.…”
Section: Safety Assurance Monitor -Sammentioning
confidence: 99%