A neurodynamic optimization approach to robust pole assignment for synthesizing linear control systems based on a convex feasibility problem reformulation

Xinyi Le, Jun Wang

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

3 Citations (Scopus)

Abstract

A neurodynamic optimization approach to robust pole assignment for synthesizing linear control systems is presented in this paper. The problem is reformulated from a quasi-convex optimization problem into a convex feasibility problem with the spectral condition number as the robustness measure. Two coupled globally convergent recurrent neural networks are applied for solving the reformulated problem in real time. Robust parametric configuration and exact pole assignment of feedback control systems can be achieved. Simulation results of the proposed neurodynamic approach are reported to demonstrate its effectiveness.

Original languageEnglish
Title of host publicationNeural Information Processing - 20th International Conference, ICONIP 2013, Proceedings
Pages284-291
Number of pages8
EditionPART 1
DOIs
Publication statusPublished - 2013
Externally publishedYes
Event20th International Conference on Neural Information Processing, ICONIP 2013 - Daegu, Korea, Republic of
Duration: 3 Nov 20137 Nov 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume8226 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference20th International Conference on Neural Information Processing, ICONIP 2013
Country/TerritoryKorea, Republic of
CityDaegu
Period3/11/137/11/13

Keywords

  • Global convergence
  • Recurrent neural networks
  • Robust pole assignment
  • State feedback control

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