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

Xinyi Le, Jun Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

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 languageEnglish
Title of host publication2014 IEEE Conference on Control Applications, CCA. Part of 2014 IEEE Multi-conference on Systems and Control, MSC 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1425-1430
Number of pages6
ISBN (Electronic)9781479974092
DOIs
Publication statusPublished - 9 Dec 2014
Externally publishedYes
Event2014 IEEE Conference on Control Applications, CCA 2014 - Juan Les Antibes, France
Duration: 8 Oct 201410 Oct 2014

Publication series

Name2014 IEEE Conference on Control Applications, CCA. Part of 2014 IEEE Multi-conference on Systems and Control, MSC 2014

Conference

Conference2014 IEEE Conference on Control Applications, CCA 2014
Country/TerritoryFrance
CityJuan Les Antibes
Period8/10/1410/10/14

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