Adaptive parameter estimation with guaranteed prescribed performance

Juan Yang, Jing Na, Xing Wu, Yu Guo

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

10 Citations (Scopus)

Abstract

This paper presents a novel adaptive parameter estimation framework for linearly parameterized nonlinear systems, which can guarantee the prescribed error convergence performance (e.g. overshoot, convergence rate). By introducing appropriate filter operations, an explicit expression of parameter estimation error is obtained. Then a prescribed performance function (PPF) and the associate transform are proposed, such that parameter estimation can be reduced as a regulation problem of the transformed system by designing an adaptive law. To this end, a novel adaptive law based on the obtained parameter estimation error is developed, such that the error convergence can be guaranteed to be within the prescribed bound. The parameter estimation is obtained without using the measurement of the state derivatives and is independent of any observer/predictor design. Simulation results illustrate that the proposed methods can achieve faster transient and better steady-state performance than some available results.

Original languageEnglish
Title of host publication26th Chinese Control and Decision Conference, CCDC 2014
PublisherIEEE Computer Society
Pages2515-2520
Number of pages6
ISBN (Print)9781479937066
DOIs
Publication statusPublished - 1 Jan 2014
Event26th Chinese Control and Decision Conference, CCDC 2014 - Changsha, United Kingdom
Duration: 31 May 20142 Jun 2014

Conference

Conference26th Chinese Control and Decision Conference, CCDC 2014
CountryUnited Kingdom
CityChangsha
Period31/05/142/06/14

Keywords

  • Adaptive System
  • Parameter Estimation
  • Prescribed Performance
  • System Identification

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