Receding horizon neural H control for a class of nonlinear unknown systems

Choon Ki Ahn, Soo Hee Han, Wook Hyun Kwon

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

in this paper, we present new RHNHC (Receding Horizon Neural H Control) for nonlinear unknown systems. First, we propose LMI (Linear Matrix Inequality) condition on the terminal weighting matrix for stabilizing RHNHC. Under this condition, noninceasing monotonicity of the saddle point value of the finite horizon dynamic game is shown to be guaranteed. Then, we propose RHNHC for nonlinear unknown systems which guarantees the infinite horizon H norm bound and the internal stability of the closed-loop systems. Since RHNHC can deal with input and state constraints in optimization problem effectively, it does not cause an instability problem or give a poor performance in contrast to the existing neural H control schemes.

Original languageEnglish
Title of host publicationProceedings of the 16th IFAC World Congress, IFAC 2005
PublisherIFAC Secretariat
Pages960-965
Number of pages6
ISBN (Print)008045108X, 9780080451084
DOIs
Publication statusPublished - 2005
Externally publishedYes

Publication series

NameIFAC Proceedings Volumes (IFAC-PapersOnline)
Volume16
ISSN (Print)1474-6670

Keywords

  • Neural networks
  • Nonlinear systems
  • Receding horizon control
  • Unknown systems

ASJC Scopus subject areas

  • Control and Systems Engineering

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