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A Direct Approach to Robustness Opti...

You, Seungil.

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A Direct Approach to Robustness Optimization.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	A Direct Approach to Robustness Optimization./
作者:	You, Seungil.
面頁冊數:	205 p.
附註:	Source: Dissertation Abstracts International, Volume: 77-02(E), Section: B.
Contained By:	Dissertation Abstracts International77-02B(E).
標題:	Systems science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3723759
ISBN:	9781339066875

A Direct Approach to Robustness Optimization.
You, Seungil.

A Direct Approach to Robustness Optimization. - 205 p.

Source: Dissertation Abstracts International, Volume: 77-02(E), Section: B.

Thesis (Ph.D.)--California Institute of Technology, 2016.

This dissertation reformulates and streamlines the core tools of robustness analysis for linear time invariant systems using now-standard methods in convex optimization. In particular, robust performance analysis can be formulated as a primal convex optimization in the form of a semidefinite program using a semidefinite representation of a set of Gramians. The same approach with semidefinite programming duality is applied to develop a linear matrix inequality test for well-connectedness analysis, and many existing results such as the Kalman-Yakubovich--Popov lemma and various scaled small gain tests are derived in an elegant fashion. More importantly, unlike the classical approach, a decision variable in this novel optimization framework contains all inner products of signals in a system, and an algorithm for constructing an input and state pair of a system corresponding to the optimal solution of robustness optimization is presented based on this information. This insight may open up new research directions, and as one such example, this dissertation proposes a semidefinite programming relaxation of a cardinality constrained variant of the H infinity norm, which we term sparse H infinity analysis, where an adversarial disturbance can use only a limited number of channels. Finally, sparse H infinity analysis is applied to the linearized swing dynamics in order to detect potential vulnerable spots in power networks.

ISBN: 9781339066875Subjects--Topical Terms:

3168411
Systems science.

A Direct Approach to Robustness Optimization.
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245 1 2 $a A Direct Approach to Robustness Optimization.
300 $a 205 p.
500 $a Source: Dissertation Abstracts International, Volume: 77-02(E), Section: B.
500 $a Adviser: John C. Doyle.
502 $a Thesis (Ph.D.)--California Institute of Technology, 2016.
520 $a This dissertation reformulates and streamlines the core tools of robustness analysis for linear time invariant systems using now-standard methods in convex optimization. In particular, robust performance analysis can be formulated as a primal convex optimization in the form of a semidefinite program using a semidefinite representation of a set of Gramians. The same approach with semidefinite programming duality is applied to develop a linear matrix inequality test for well-connectedness analysis, and many existing results such as the Kalman-Yakubovich--Popov lemma and various scaled small gain tests are derived in an elegant fashion. More importantly, unlike the classical approach, a decision variable in this novel optimization framework contains all inner products of signals in a system, and an algorithm for constructing an input and state pair of a system corresponding to the optimal solution of robustness optimization is presented based on this information. This insight may open up new research directions, and as one such example, this dissertation proposes a semidefinite programming relaxation of a cardinality constrained variant of the H infinity norm, which we term sparse H infinity analysis, where an adversarial disturbance can use only a limited number of channels. Finally, sparse H infinity analysis is applied to the linearized swing dynamics in order to detect potential vulnerable spots in power networks.
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