A neurodynamic optimization approach to robust pole assignment based on convex reformulation

Xinyi Le; Jun Wang

doi:10.1109/CCA.2014.6981524

A neurodynamic optimization approach to robust pole assignment based on convex reformulation

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

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Abstract

Another neurodynamic optimization approach to robust pole assignment is presented for synthesizing linear control systems. The original pseudoconvex optimization problem for robust pole assignment is reformulated as a convex optimization problem. Three coupled recurrent neural networks operating in three different time scales are developed for solving the reformulated problem in real time. It is shown that robust parametric configuration and exact pole assignment of feedback control systems can be achieved. Two examples of the proposed approach are discussed in detail to demonstrate its effectiveness.

Original language	English
Title of host publication	2014 IEEE Conference on Control Applications, CCA 2014
Publisher	IEEE
Pages	1425-1430
ISBN (Print)	9781479974092
DOIs	https://doi.org/10.1109/CCA.2014.6981524
Publication status	Published - 9 Dec 2014
Externally published	Yes
Event	2014 IEEE Conference on Control Applications, CCA 2014 - Juan Les Antibes, France Duration: 8 Oct 2014 → 10 Oct 2014

Conference

Conference	2014 IEEE Conference on Control Applications, CCA 2014
Place	France
City	Juan Les Antibes
Period	8/10/14 → 10/10/14

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title = "A neurodynamic optimization approach to robust pole assignment based on convex reformulation",

abstract = "Another neurodynamic optimization approach to robust pole assignment is presented for synthesizing linear control systems. The original pseudoconvex optimization problem for robust pole assignment is reformulated as a convex optimization problem. Three coupled recurrent neural networks operating in three different time scales are developed for solving the reformulated problem in real time. It is shown that robust parametric configuration and exact pole assignment of feedback control systems can be achieved. Two examples of the proposed approach are discussed in detail to demonstrate its effectiveness.",

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year = "2014",

month = dec,

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doi = "10.1109/CCA.2014.6981524",

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note = "2014 IEEE Conference on Control Applications, CCA 2014 ; Conference date: 08-10-2014 Through 10-10-2014",

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AU - Le, Xinyi

AU - Wang, Jun

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AB - Another neurodynamic optimization approach to robust pole assignment is presented for synthesizing linear control systems. The original pseudoconvex optimization problem for robust pole assignment is reformulated as a convex optimization problem. Three coupled recurrent neural networks operating in three different time scales are developed for solving the reformulated problem in real time. It is shown that robust parametric configuration and exact pole assignment of feedback control systems can be achieved. Two examples of the proposed approach are discussed in detail to demonstrate its effectiveness.

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U2 - 10.1109/CCA.2014.6981524

DO - 10.1109/CCA.2014.6981524

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9781479974092

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EP - 1430

BT - 2014 IEEE Conference on Control Applications, CCA 2014

PB - IEEE

T2 - 2014 IEEE Conference on Control Applications, CCA 2014

Y2 - 8 October 2014 through 10 October 2014

ER -

A neurodynamic optimization approach to robust pole assignment based on convex reformulation

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