2019 IEEE 58th Conference on Decision and Control (CDC) 2019
DOI: 10.1109/cdc40024.2019.9029831
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The H∞,p norm as the differential L2,p gain of a p-dominant system

Abstract: The differential L2,p gain of a linear, timeinvariant, p-dominant system is shown to coincide with the H∞,p norm of its transfer function G, defined as the essential supremum of the absolute value of G over a vertical strip in the complex plane such that p poles of G lie to right of the strip. The close analogy between the H∞,p norm and the classical H∞ norm suggests that robust dominance of linear systems can be studied along the same lines as robust stability. This property can be exploited in the analysis a… Show more

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Cited by 11 publications
(13 citation statements)
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“…Combining the notion of p-gain and the small gain theorem below [34], we have a framework for robust control of dominant systems, as in classical robust stability.…”
Section: P-dissipativity and Lmismentioning
confidence: 99%
“…Combining the notion of p-gain and the small gain theorem below [34], we have a framework for robust control of dominant systems, as in classical robust stability.…”
Section: P-dissipativity and Lmismentioning
confidence: 99%
“…The dominance properties of an interconnected system can be studied using small L 2,p -gain conditions (Forni and Sepulchre, 2019;Padoan et al, 2019b). Theorem 2.…”
Section: Dominance Theorymentioning
confidence: 99%
“…For a quantitative perspective, the robustness of p-dominance property of the mixed-feedback amplifier can be studied using small-gain results, adapted to dominance through the notion of p-gain. Not surprisingly, the feedback interconnection of a p-dominant system with 0-dominant uncertainties ∆ l , ∆ p , and ∆ n remains p-dominant if the product of their gains is sufficiently small (their product must be less than one, as in the classical stability [18], [24]).…”
Section: Robustness To Unmodeled Dynamicsmentioning
confidence: 99%
“…This means that the stable / oscillatory regimes of the mixed feedback are robust. A quantitative analysis can be developep through convex optimization, based on linear matrix inequalities [18], [24].…”
Section: Robustness To Unmodeled Dynamicsmentioning
confidence: 99%