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Stability of switched systems with average dwell-time

Hespanha, J.P., Morse, A.S.·2003·Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304)

Abstract

It is shown that switching among stable linear systems results in a stable system provided that switching is "slow-on-the-average". In particular, it is proved that exponential stability is achieved when the number of switches in any finite interval grows linearly with the length of the interval, and the growth rate is sufficiently small. Moreover, the exponential stability is uniform over all switchings with the above property. For switched systems with inputs this guarantees that several input-to-state induced norms are bounded uniformly over all slow-on-the-average switchings. These results extend to classes of nonlinear switched systems that satisfy suitable uniformity assumptions. In this paper it is also shown that, in a supervisory control context, scale-independent hysteresis can produce switching that is slow-on-the-average and therefore the results mentioned above can be used to study the stability of hysteresis-based adaptive control systems.

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Metadata

Authors
Hespanha, J. P., & Morse, A. S.
Venue
Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304)
Publisher
IEEE
Published
2003-01-22
Volume
3
Pages
2655-2660
ISSN
0191-2216 (ISSN-L)
OpenAlex
W1900371862
Cited by
2,588
References
0
FWCI
75.04
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Stability of switched systems with average dwell-time. https://app.cochat.ai/papers/10.1109/cdc.1999.831330
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Hespanha, J. P., & Morse, A. S. (2003). Stability of switched systems with average dwell-time. Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304). https://doi.org/10.1109/cdc.1999.831330

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.