Robust adaptive finite-time parameter estimation for linearly parameterized nonlinear systems

Jing Na, Muhammad Nasiruddin Mahyuddin, Guido Herrmann, Xuemei Ren

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

51 Citations (Scopus)


This paper studies a novel adaptive parameter estimation framework for linearly parameterized nonlinear systems. Appropriate parameter error information is derived by defining auxiliary filtered variables and used to drive the parameter adaptation, which guarantees exponential error convergence. The proposed method is further improved via a sliding mode approach to achieve finite-time (FT) error convergence. The case with bounded disturbances or noises is also studied. The parameter estimation is obtained without using the state derivatives and is independent of observer/predictor design. The online computation of the regressor matrix inverse can be avoided. Simulation examples are included to illustrate the effectiveness.

Original languageEnglish
Title of host publicationChinese Control Conference, CCC
PublisherIEEE Computer Society
Number of pages7
ISBN (Print)9789881563835
Publication statusPublished - 18 Oct 2013
Event32nd Chinese Control Conference, CCC 2013 - Xi'an, United Kingdom
Duration: 26 Jul 201328 Jul 2013


Conference32nd Chinese Control Conference, CCC 2013
Country/TerritoryUnited Kingdom


  • Adaptive system
  • Finite time convergence
  • Parameter estimation


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