2020
DOI: 10.1016/j.automatica.2020.108821
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Feedback stabilization for a bilinear control system under weak observability inequalities

Abstract: In this paper, we discuss the feedback stabilization of bilinear systems under weak observation properties. In this case, the uniform stability is not guaranteed. Thus we provide an explicit weak decay rate for all regular initial data. Applications to Schrödinger and wave equations are provided.2010 Mathematics Subject Classification. 93D15.

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Cited by 16 publications
(5 citation statements)
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“…It is worth noting that there are several works that are interested in the question U ad ̸ = ∅ (see for instance [3,4,14,24]).…”
Section: Proofmentioning
confidence: 99%
“…It is worth noting that there are several works that are interested in the question U ad ̸ = ∅ (see for instance [3,4,14,24]).…”
Section: Proofmentioning
confidence: 99%
“…In the case where X=H is a Hilbert space, the feedback stabilization problem of () has been studied by many authors (see previous research [8–13]). In Ball and Slemrod [9], the authors studied the weak stabilization of () with the control vfalse(tfalse)=yfalse(tfalse),Byfalse(tfalse), under the assumption: BSfalse(tfalse)ξ,Sfalse(tfalse)ξ=0,2.56804ptt0ξ=0. …”
Section: Introductionmentioning
confidence: 99%
“…More recently, Zaitsev [23] considered discrete‐time bilinear systems with periodic coefficients and obtained sufficient conditions for uniform global asymptotic stabilization of the origin by state feedback. In infinite dimension, the stabilization of bilinear systems has been studied by many authors in the continuous‐time case, such as Ouzahra [24] and Ammari and Ouzahra [10]. However, in the discrete‐time case, the question has seldom been addressed.…”
Section: Introductionmentioning
confidence: 99%
“…Thus, in practice, they are used to model a large variety of real-world processes [1][2][3][4][5][6][7][8]. Stabilization of bilinear systems is a fundamental issue in control theory and has been well investigated over the last few decades by many authors especially in the continuous time case [9][10][11][12][13][14][15][16][17][18][19][20][21]. In practice, modeling errors, such as noise disturbance and parameter uncertainties, are unavoidable.…”
Section: Introductionmentioning
confidence: 99%