Adaptive asymptotic stabilization for stochastic nonlinear systems with dynamic uncertainties

Shu Jun Liu, Ji Feng Zhang, Zhong Ping Jiang

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

Abstract

In this paper, the problem of adaptive stabilization is investigated for stochastic nonlinear systems with three types of uncertainties: parametric uncertainties, uncertain nonlinearities and unmodeled dynamics. Under the assumption that the unmodeled dynamics are stochastic input-to-state stable, for the general smooth systems in which both drift and diffusion vector fields depend on not only the output but also the unmodeled dynamics, an adaptive output-feedback controller is constructively designed by the methods of adaptive backstepping with tuning function and changing the supply function. It is shown that under mild conditions, the closed-loop system is bounded in probability and moreover, the output can be regulated to the origin almost surely when the drift and diffusion vector fields vanish at the origin.

Original languageEnglish (US)
Title of host publication2006 Chinese Control Conference Proceedings, CCC 2006
PublisherIEEE Computer Society
Pages2064-2069
Number of pages6
ISBN (Print)7810778021, 9787810778022
DOIs
StatePublished - 2006
Event25th Chinese Control Conference, CCC 2006 - Harbin, China
Duration: Aug 7 2006Aug 11 2006

Publication series

Name2006 Chinese Control Conference Proceedings, CCC 2006

Other

Other25th Chinese Control Conference, CCC 2006
CountryChina
CityHarbin
Period8/7/068/11/06

Keywords

  • Adaptive control
  • Stochastic input-to-state stable (SISS)
  • Unmodeled dynamics

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Applied Mathematics
  • Modeling and Simulation
  • Computer Science Applications

Fingerprint Dive into the research topics of 'Adaptive asymptotic stabilization for stochastic nonlinear systems with dynamic uncertainties'. Together they form a unique fingerprint.

Cite this