On a stabilization problem of nonlinear programming neural networks

Yuan Can Huang, Chuang Yu, Lingyun Zhu

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

1 Citation (Scopus)

Abstract

Intrinsically, Lagrange multipliers in Nonlinear Programming Theory play a regulating role in the process of searching the optima of constrained optimization problems. Hence, they may be regarded as control input variables as those in control systems. From this new perspective, it is showed that synthesizing nonlinear programming neural networks can be formulated to solve servomechanism problems. In this paper, under the second-order sufficient assumptions of nonlinear programming problems, a dynamic output feedback control law is proposed to stabilize the corresponding nonlinear programming neural networks. Moreover, their asymptotical stability is proved by the Lyapunov First Approximation Principle.

Original languageEnglish
Title of host publicationProceedings of the 17th World Congress, International Federation of Automatic Control, IFAC
Edition1 PART 1
DOIs
Publication statusPublished - 2008
Event17th World Congress, International Federation of Automatic Control, IFAC - Seoul, Korea, Republic of
Duration: 6 Jul 200811 Jul 2008

Publication series

NameIFAC Proceedings Volumes (IFAC-PapersOnline)
Number1 PART 1
Volume17
ISSN (Print)1474-6670

Conference

Conference17th World Congress, International Federation of Automatic Control, IFAC
Country/TerritoryKorea, Republic of
CitySeoul
Period6/07/0811/07/08

Keywords

  • Regulation
  • Stability of NL systems
  • Static optimization problems

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